Search result: Catalogue data in Spring Semester 2023
Physics Master  
Electives  
General Electives Students may choose General Electives from the entire course programme of ETH Zurich  with the following restrictions: courses that belong to the first or second year of a Bachelor curriculum at ETH Zurich as well as courses from GESS "Science in Perspective" are not eligible here. The following courses are explicitly recommended to physics students by their lecturers. (Courses in this list may be assigned to the category "General Electives" directly in myStudies. For the category assignment of other eligible courses keep the choice "no category" and take contact with the Study Administration (Link) after having received the credits.)  
Number  Title  Type  ECTS  Hours  Lecturers  

101017801L  Uncertainty Quantification in Engineering  W  3 credits  2G  N. Lüthen  
Abstract  Uncertainty quantification aims at studying the impact of aleatory and epistemic uncertainty onto computational models used in science and engineering. The course introduces the basic concepts of uncertainty quantification: probabilistic modelling of data (copula theory), uncertainty propagation techniques (Monte Carlo simulation, polynomial chaos expansions), and sensitivity analysis.  
Objective  After this course students will be able to properly pose an uncertainty quantification problem, select the appropriate computational methods and interpret the results in meaningful statements for field scientists, engineers and decision makers. The course is suitable for any master/Ph.D. student in engineering or natural sciences, physics, mathematics, computer science with a basic knowledge of probability theory.  
Content  The course introduces uncertainty quantification through a set of practical case studies that come from civil, mechanical, nuclear and electrical engineering, from which a general framework is introduced. The course in then divided into three blocks: probabilistic modelling (introduction to copula theory), uncertainty propagation (Monte Carlo simulation and polynomial chaos expansions) and sensitivity analysis (correlation measures, Sobol' indices). Each block contains lectures and tutorials using Matlab and the inhouse software UQLab (Link).  
Lecture notes  Detailed slides are provided for each lecture. A printed script gathering all the lecture slides may be bought at the beginning of the semester.  
Prerequisites / Notice  A basic background in probability theory and statistics (bachelor level) is required. A summary of useful notions will be handed out at the beginning of the course. A good knowledge of Matlab is required to participate in the tutorials and for the miniproject.  
Competencies 
 
151016000L  Fuel Cycle and Waste Management Note: The previous course title until FS22 "Nuclear Energy Systems".  W  4 credits  2V + 1U  R. Eichler, S. Churakov, T. Kämpfer, M. Streit  
Abstract  Physical and chemical aspects of the synthesis and distribution of uranium, radioactive decay and detection, uranium production, uranium enrichment, nuclear fuel production, reprocessing of spent fuel, nuclear waste disposal and final deep geological repository  
Objective  Students get an overview on the physical and chemical fundamentals, the technological processes and the environmental impact of the full energy conversion chain of nuclear power generation including final repository.  
Content  (15) survey on the cosmic and geological origin of uranium and its deposits, (radio) chemical fundamentals relevant for uranium handling, radiaoctive decay and its detection; (69) methods of uranium mining, separation of uranium from the ore, enrichment of uranium (diffusion cells, ultracentrifuges, alternative methods), chemical conversion uranium oxid  fluorid  oxid, fuel rod fabrication processes, fuel reprocessing (hydrochemical, pyrochemical) including modern developments of deep partitioning as well as methods to treat and minimize the amount and radiotoxicity of nuclear waste. (1013) nuclear waste disposal, waste categories and origin, geological and engineered barriers in deep geological repositories, the project of a deep geological disposal for radioactive waste in Switzerland  
Lecture notes  Lecture slides will be distributed as handouts and in digital form  
151015600L  Technology and Safety of Nuclear Power Plants Note: The previous course title until FS22 "Safety of Nuclear Power Plants".  W  6 credits  4V + 1U  A. Manera  
Abstract  Knowledge about safety concepts and requirements of nuclear power plants and their implementation in deterministic safety concepts and safety systems. Knowledge about behavior under accident conditions and about the methods of probabilistic risk analysis and how to handle results. Introduction into key elements of the enhanced safety of nuclear systems for the future.  
Objective  Deep understanding of safety requirements, concepts and system of nuclear power plants, knowledge of deterministic and probabilistic methods for safety analysis, aspects of nuclear safety research, licensing of nuclear power plant operation. Overview on key elements of the enhanced safety of nuclear systems for the future.  
Content  (1) Introduction into the specific safety issues of nuclear power plants, main facts of health effects of ionizing radiation, defense in depth approach. (2) Reactor protection and reactivity control, reactivity induced accidents (RIA). (3) Lossofcoolant accidents (LOCA), emergency core cooling systems. (4) Short introduction into severe accidents (Beyond Design Base Accidents, BDBA). (5) Probabilistic risk analysis (PRA level 1,2,3). (6) Passive safety systems. (7) Safety of innovative reactor concepts.  
Lecture notes  Script: Handouts of lecture slides will be distributed Script "Short introduction into basics of nuclear power"  
Literature  S. Glasston & A. Sesonke: Nuclear Reactor Engineering, Reactor System Engineering, Ed. 4, Vol. 2., Chapman & Hall, NY, 1994  
Prerequisites / Notice  Prerequisites: Recommended in advance (not binding): 151016300L Nuclear Energy Conversion  
151016600L  Physics of Nuclear Reactor II  W  4 credits  3G  K. Mikityuk  
Abstract  Reactor physics calculations for assessing the performance and safety of nuclear power plants are, in practice, carried out using large computer codes simulating different key phenomena. This course provides a basis for understanding stateoftheart calculational methodologies in the above context.  
Objective  Students are introduced to advanced methods of reactor physics analysis for nuclear power plants.  
Content  Crosssections preparation. Slowing down theory. Differential form of the neutron transport equation and method of discrete ordinates (Sn). Integral form of the neutron transport equation and method of characteristics. Method of MonteCarlo. Modeling of fuel depletion. Lattice calculations and crosssection parametrization. Modeling of full core neutronics using nodal methods. Modeling of feedbacks from fuel behavior and thermal hydraulics. Point and spatial reactor kinetics. Uncertainty and sensitivity analysis.  
Lecture notes  Handouts will be provided on the website.  
Literature  Chapters from various text books on Reactor Theory, etc.  
151190600L  Multiphase Flows  W  4 credits  3G  F. Coletti  
Abstract  Introduction to fluid flows with multiple interacting phases. The emphasis is on regimes where a dispersed phase is carried by a continuous one: e.g., particles, bubbles and droplets suspended in gas or liquid flows, laminar or turbulent. The flow physics is put in the context of natural, biological, and industrial problems.  
Objective  The main learning objectives are:  identify multiphase flow regimes and relevant nondimensional parameters  distinguish spatiotemporal scales at play for each phase  quantify mutual coupling between different phases  apply fundamental principles in complex realworld flows  combine insight from theory, experiments, and numerics  
Content  Single particle and multiparticle dynamics in laminar and turbulent flows; basics of suspension rheology; effects of surface tension on the formation, evolution and motion of bubbles and droplets; freesurface flows and windwave interaction; imaging techniques and modeling approaches.  
Lecture notes  Lecture slides are made available.  
Literature  Suggested readings are provided for each topic.  
Prerequisites / Notice  Fundamental knowledge of fluid dynamics is essential.  
151053000L  Nonlinear Dynamics and Chaos II  W  4 credits  4G  G. Haller  
Abstract  The internal structure of chaos; Hamiltonian dynamical systems; Normally hyperbolic invariant manifolds; Geometric singular perturbation theory; Finitetime dynamical systems  
Objective  The course introduces the student to advanced, comtemporary concepts of nonlinear dynamical systems analysis.  
Content  I. The internal structure of chaos: symbolic dynamics, Bernoulli shift map, subshifts of finite type; chaos is numerical iterations. II.Hamiltonian dynamical systems: conservation and recurrence, stability of fixed points, integrable systems, invariant tori, LiouvilleArnoldJost Theorem, KAM theory. III. Normally hyperbolic invariant manifolds: Crash course on differentiable manifolds, existence, persistence, and smoothness, applications. IV. Geometric singular perturbation theory: slow manifolds and their stability, physical examples. V. Finitetime dynamical system; detecting Invariant manifolds and coherent structures in finitetime flows  
Lecture notes  Handwritten instructor's notes and typed lecture notes will be downloadable from Moodle.  
Literature  Books will be recommended in class  
Prerequisites / Notice  Nonlinear Dynamics I (151053200) or equivalent  
151062000L  Embedded MEMS Lab  W  5 credits  3P  C. Hierold, M. Haluska  
Abstract  Practical course: Students are introduced to the process steps required for the fabrication of MEMS (Micro Electro Mechanical System) and carry out the fabrication and testing steps in the clean rooms themselves. Additionally, they learn the requirements for working in clean rooms. Processing and characterization will be documented and analyzed in a final report.  
Objective  Students learn the individual process steps that are required to make a MEMS (Micro Electro Mechanical System). Students carry out the process steps themselves in laboratories and clean rooms. Furthermore, participants become familiar with the special requirements (cleanliness, safety, operation of equipment and handling hazardous chemicals) of working in the clean rooms and laboratories. The entire production, processing, and characterization of the MEMS is documented and evaluated in a final report.  
Content  With guidance from a tutor, the individual silicon microsystem process steps that are required for the fabrication of an accelerometer are carried out:  Photolithography, dry etching, wet etching, sacrificial layer etching, various cleaning procedures  Packaging and electrical connection of a MEMS device  Testing and characterization of the MEMS device  Written documentation and evaluation of the entire production, processing and characterization  
Lecture notes  A document containing theory, background and practical course content is distributed in the informational meeting.  
Literature  The document provides sufficient information for the participants to successfully participate in the course.  
Prerequisites / Notice  Participating students are required to attend all scheduled lectures and meetings of the course. Participating students are required to provide proof that they have personal accident insurance prior to the start of the laboratory portion of the course. This master's level course is limited to 15 students per semester for safety and efficiency reasons. If there are more than 15 students registered, we regret to restrict access to this course by the following rules: Priority 1: master students of the master's program in "Micro and Nanosystems" Priority 2: master students of the master's program in "Mechanical Engineering" with a specialization in Microsystems and Nanoscale Engineering (MAVTtutors Profs Hierold, Koumoutsakos, Nelson, Norris, Poulikakos, Pratsinis, Stemmer), who attended the bachelor course "151062100L Microsystems Technology" successfully. Priority 3: master students, who attended the bachelor course "151062100L Microsystems Technology" successfully. Priority 4: all other students (PhD, bachelor, master) with a background in silicon or microsystems process technology. If there are more students in one of these priority groups than places available, we will decide with respect to (in following order) best achieved grade from 151062100L Microsystems Technology, registration to this practicum at previous semester, and by drawing lots. Students will be notified at the first lecture of the course (introductory lecture) as to whether they are able to participate. The course is offered in autumn and spring semester.  
151092800L  CO2 Capture and Storage and the Industry of CarbonBased Resources  W  4 credits  3G  A. Bardow, V. Becattini, N. Gruber, M. Mazzotti, M. Repmann, T. Schmidt, D. Sutter  
Abstract  This course introduces the fundamentals of carbon capture, utilization, and storage and related interdependencies between technosphere, ecosphere, and sociosphere. Topics covered: origin, production, processing, and economics of carbonbased resources; climate change in science & policies; CC(U)S systems; CO2 transport & storage; lifecycle assessment; netzero emissions; CO2 removal options.  
Objective  The lecture aims to introduce carbon dioxide capture, utilization, and storage (CCUS) systems, the technical solutions developed so far, and current research questions. This is done in the context of the origin, production, processing, and economics of carbonbased resources and of climate change issues. After this course, students are familiar with relevant technical and nontechnical issues related to using carbon resources, climate change, and CCUS as a mitigation measure. The class will be structured in 2 hours of lecture and one hour of exercises/discussion.  
Content  The transition to a netzero society is associated with major challenges in all sectors, including energy, transportation, and industry. In the IPCC Special Report on Global Warming of 1.5 °C, rapid emission reduction and negative emission technologies are crucial to limiting global warming to below 1.5 °C. Therefore, this course illuminates carbon capture, utilization, and storage as a potential set of technologies for emission mitigation and for generating negative emissions.  
Lecture notes  Lecture slides and supplementary documents will be available online.  
Literature  IPCC Special Report on Global Warming of 1.5°C, 2018. Link IPCC AR5 Climate Change 2014: Synthesis Report, 2014. Link IPCC AR6 Climate Change 2022: Mitigation of Climate Change, 2022. Link Global Status of CCS 2020. Published by the Global CCS Institute, 2020. Link  
Prerequisites / Notice  External lecturers from the industry and other institutes will contribute with specialized lectures according to the schedule distributed at the beginning of the semester.  
227104600L  Computer Simulations of Sensory Systems  W  3 credits  3G  T. Haslwanter  
Abstract  This course deals with computer simulations of the human auditory, visual, and balance system. The lecture will cover the physiological and mechanical mechanisms of these sensory systems. And in the exercises, the simulations will be implemented with Python. The simulations will be such that their output could be used as input for actual neurosensory prostheses.  
Objective  Our sensory systems provide us with information about what is happening in the world surrounding us. Thereby they transform incoming mechanical, electromagnetic, and chemical signals into “action potentials”, the language of the central nervous system. The main goal of this lecture is to describe how our sensors achieve these transformations, how they can be reproduced with computational tools. For example, our auditory system performs approximately a “Fourier transformation” of the incoming sound waves; our early visual system is optimized for finding edges in images that are projected onto our retina; and our balance system can be well described with a “control system” that transforms linear and rotational movements into nerve impulses. In the exercises that go with this lecture, we will use Python to reproduce the transformations achieved by our sensory systems. The goal is to write programs whose output could be used as input for actual neurosensory prostheses: such prostheses have become commonplace for the auditory system, and are under development for the visual and the balance system. For the corresponding exercises, at least some basic programing experience is required!  
Content  The following topics will be covered: • Introduction into the signal processing in nerve cells. • Introduction into Python. • Simplified simulation of nerve cells (HodgkinsHuxley model). • Description of the auditory system, including the application of Fourier transforms on recorded sounds. • Description of the visual system, including the retina and the information processing in the visual cortex. The corresponding exercises will provide an introduction to digital image processing. • Description of the mechanics of our balance system, and the “Control System”language that can be used for an efficient description of the corresponding signal processing (essentially Laplace transforms and control systems).  
Lecture notes  For each module additional material will be provided on the elearning platform "moodle". The main content of the lecture is also available as a wikibook, under Link  
Literature  Open source information is available as wikibook Link For good overviews of the neuroscience, I recommend: • Principles of Neural Science (5th Ed, 2012), by Eric Kandel, James Schwartz, Thomas Jessell, Steven Siegelbaum, A.J. Hudspeth ISBN 0071390111 / 9780071390118 THE standard textbook on neuroscience. NOTE: The 6th edition will be released on February 5, 2021! • L. R. Squire, D. Berg, F. E. Bloom, Lac S. du, A. Ghosh, and N. C. Spitzer. Fundamental Neuroscience, Academic Press  Elsevier, 2012 [ISBN: 9780123858702]. This book covers the biological components, from the functioning of an individual ion channels through the various senses, all the way to consciousness. And while it does not cover the computational aspects, it nevertheless provides an excellent overview of the underlying neural processes of sensory systems. • G. Mather. Foundations of Sensation and Perception, 2nd Ed Psychology Press, 2009 [ISBN: 9781841696980 (hardcover), oder 9781841696997 (paperback)] A coherent, uptodate introduction to the basic facts and theories concerning human sensory perception. • The best place to get started with Python programming are the Link On signal processing with Python, my upcoming book • Handson Signal Analysis with Python (Due: January 13, 2021 ISBN 9783030579029, Link) will contain an explanation to all the required programming tools and packages.  
Prerequisites / Notice  •Since I have to travel from Linz, Austria, to Zurich to give this lecture, I plan to hold this lecture online every 2nd week. In addition to the lectures, this course includes external lab visits to institutes actively involved in research on the relevant sensory systems.  
227014700L  VLSI 2: From Netlist to Complete System on Chip  W  6 credits  5G  F. K. Gürkaynak, L. Benini  
Abstract  This second course in our VLSI series is concerned with how to turn digital circuit netlists into safe, testable and manufacturable mask layout, taking into account various parasitic effects. Lowpower circuit design is another important topic. Economic aspects and management issues of VLSI projects round off the course.  
Objective  Know how to design digital VLSI circuits that are safe, testable, durable, and make economic sense.  
Content  The second course begins with a thorough discussion of various technical aspects at the circuit and layout level before moving on to economic issues of VLSI. Topics include:  The difficulties of finding fabrication defects in large VLSI chips.  How to make integrated circuit testable (design for test).  Synchronous clocking disciplines compared, clock skew, clock distribution, input/output timing.  Synchronization and metastability.  CMOS transistorlevel circuits of gates, flipflops and random access memories.  Sinks of energy in CMOS circuits.  Power estimation and lowpower design.  Current research in lowenergy computing.  Layout parasitics, interconnect delay, static timing analysis.  Switching currents, ground bounce, IRdrop, power distribution.  Floorplanning, chip assembly, packaging.  Layout design at the mask level, physical design verification.  Electromigration, electrostatic discharge, and latchup.  Models of industrial cooperation in microelectronics.  The caveats of virtual components.  The cost structures of ASIC development and manufacturing.  Market requirements, decision criteria, and case studies.  Yield models.  Avenues to lowvolume fabrication.  Marketing considerations and case studies.  Management of VLSI projects. Exercises are concerned with backend design (floorplanning, placement, routing, clock and power distribution, layout verification). Industrial CAD tools are being used.  
Lecture notes  H. Kaeslin: "TopDown Digital VLSI Design, from GateLevel Circuits to CMOS Fabrication", Lecture Notes Vol.2 , 2015. All written documents in English.  
Literature  H. Kaeslin: "TopDown Digital VLSI Design, from Architectures to GateLevel Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303.  
Prerequisites / Notice  Highlight: Students are offered the opportunity to design a circuit of their own which then gets actually fabricated as a microchip! Students who elect to participate in this program register for a term project at the Integrated Systems Laboratory in parallel to attending the VLSI II course. Prerequisites: "VLSI I: from Architectures to Very Large Scale Integration Circuits and FPGAs" or equivalent knowledge. Further details: Link  
227014800L  VLSI 4: Practical VLSI: Measurement and Testing  W  6 credits  4G  F. K. Gürkaynak, L. Benini  
Abstract  In this revamped course, we will concentrate on practical aspects of modern integrated circuit testing with an emphasis on handsonexperience on an IC tester. This will help students to better understand several aspects that have been highlighted in previous VLSI lecture series and allow them to test their own ICs designed during prior semester/bachelor theses.  
Objective  In this course, students will:  Get handson experience working in a modern IC Test laboratory and learn the steps needed to bringup, characterize and test digital integrated circuits.  Develop problem solving skills and get experience in approaching issues that involve many different engineering steps.  Gather first hand experience how DesignForTest (DFT) methodologies help for IC Design, and understand the tradeoffs between performance and testability.  Learn about challenges of IC Manufacturing process, and what kind of failures can be encountered, and get a deeper understanding of IC Design process  For students that have worked on a prior bachelor/semester thesis on an IC design project, allow them to test their own IC.  
Content  If you want to earn money by selling ICs, you will have to deliver a product that will function properly with a very large probability. This lecture will be discussing how this can be achieved. The main point of emphasis will be handsonexercises on a stateoftheart automated test equipment (Advantest SoC V93000) where students will work in groups of two (or maximum three). Students will be able to schedule their exercises so that it fits their individual schedule. There will also be concentrated classroom lectures that will convey the necessary information that students will need for the exercises which will cover aspects of  Economics of testing  CMOS manufacturing and fault models, stuck at faults  Automated Test Equipment  Measuring timing and power  Testing of memories  Built in SelfTest (BIST) There will be 10 lectures (some weeks will be lecture free, exact schedule to be communicated) and 8 exercises. The final exercise will involve individual work where students test an IC with the knowledge they gained from previous exercises. Students that complete this exercise and present a test report (410 pages) will pass the course. Please note that the exercises in this class are involved and will require you to make preparations in advance. Expect to spend at least 4 hours of your own time for exercise preparations, and expect at least three individual half day sessions for the final exercise where you test the IC to qualify for a passing grade. It will be possible to finish the exercises until the end of July.  
Lecture notes  The following book will accompany students during the lecture: "Essentials of Electronic Testing for Digital, Memory and MixedSignal VLSI Circuits" by Michael L. Bushnell and Vishwani D. Agrawal, Springer, 2004. This book is available online within ETH through Link  
Literature  Course website: Link  
Prerequisites / Notice  VLSI4 is meant for students interested in digital IC Design and especially for students that are planning or have already done a bachelor/semester thesis on IC Design. Although not strictly necessary, VLSI2 would be quite helpful for students visiting this lecture, VLSI2 and VLSI4 can be visited at the same time. Other lectures of the VLSI series (VLSI1, VLSI3) are not needed to follow VLSI4. Course website for up to date information: Link  
227016100L  Molecular and Materials Modelling  W  6 credits  2V + 2U  D. Passerone, C. Pignedoli  
Abstract  The course introduces the basic techniques to interpret experiments with contemporary atomistic simulation, including force fields or ab initio based molecular dynamics and Monte Carlo. Structural and electronic properties will be simulated handson for realistic systems. The modern methods of "big data" analysis applied to the screening of chemical structures will be introduced with examples.  
Objective  The ability to select a suitable atomistic approach to model a nanoscale system, and to employ a simulation package to compute quantities providing a theoretically sound explanation of a given experiment. This includes knowledge of empirical force fields and insight in electronic structure theory, in particular density functional theory (DFT). Understanding the advantages of Monte Carlo and molecular dynamics (MD), and how these simulation methods can be used to compute various static and dynamic material properties. Basic understanding on how to simulate different spectroscopies (IR, Xray, UV/VIS). Performing a basic computational experiment: interpreting the experimental input, choosing theory level and model approximations, performing the calculations, collecting and representing the results, discussing the comparison to the experiment.  
Content  Classical force fields in molecular and condensed phase systems Methods for finding stationary states in a potential energy surface Monte Carlo techniques applied to nanoscience Classical molecular dynamics: extracting quantities and relating to experimentally accessible properties From molecular orbital theory to quantum chemistry: chemical reactions Condensed phase systems: from periodicity to band structure Larger scale systems and their electronic properties: density functional theory and its approximations Advanced molecular dynamics: Correlation functions and extracting free energies The use of Smooth Overlap of Atomic Positions (SOAP) descriptors in the evaluation of the (dis)similarity of crystalline, disordered and molecular compounds  
Lecture notes  A script will be made available and complemented by literature references.  
Literature  D. Frenkel and B. Smit, Understanding Molecular Simulations, Academic Press, 2002. M. P. Allen and D.J. Tildesley, Computer Simulations of Liquids, Oxford University Press 1990. C. J. Cramer, Essentials of Computational Chemistry. Theories and Models, Wiley 2004 G. L. Miessler, P. J. Fischer, and Donald A. Tarr, Inorganic Chemistry, Pearson 2014. K. Huang, Statistical Mechanics, Wiley, 1987. N. W. Ashcroft, N. D. Mermin, Solid State Physics, Saunders College 1976. E. Kaxiras, Atomic and Electronic Structure of Solids, Cambridge University Press 2010.  
227045500L  Terahertz: Technology and Applications Does not take place this semester.  W  5 credits  3G + 3A  
Abstract  This block course will provide a solid foundation for understanding physical principles of THz applications. We will discuss various building blocks of THz technology  components dealing with generation, manipulation, and detection of THz electromagnetic radiation. We will introduce THz applications in the domain of imaging, sensing, communications, nondestructive testing and evaluations.  
Objective  This is an introductory course on Terahertz (THz) technology and applications. Devices operating in THz frequency range (0.1 to 10 THz) have been increasingly studied in the recent years. Progress in nonlinear optical materials, ultrafast optical and electronic techniques has strengthened research in THz application developments. Due to unique interaction of THz waves with materials, applications with new capabilities can be developed. In theory, they can penetrate somewhat like Xrays, but are not considered harmful radiation, because THz energy level is low. They should be able to provide resolution as good as or better than magnetic resonance imaging (MRI), possibly with simpler equipment. Imaging, veryhigh bandwidth communication, and energy harvesting are the most widely explored THz application areas. We will study the basics of THz generation, manipulation, and detection. Our emphasis will be on the physical principles and applications of THz in the domain of imaging, sensing, communications, nondestructive testing and evaluations. The second part of the block course will be a short project work related to the topics covered in the lecture. The learnings from the project work should be presented in the end.  
Content  PART I:  INTRODUCTION  Chapter 1: Introduction to THz Physics Chapter 2: Components of THz Technology  THz TECHNOLOGY MODULES  Chapter 3: THz Generation Chapter 4: THz Detection Chapter 5: THz Manipulation  APPLICATIONS  Chapter 6: THz Imaging / Sensing / Communication Chapter 7: THz Nondestructive Testing Chapter 8: THz Applications in Plastic & Recycling Industries PART 2:  PROJECT WORK  Short project work related to the topics covered in the lecture. Short presentation of the learnings from the project work. Full guidance and supervision will be given for successful completion of the short project work.  
Lecture notes  Softcopy of lectures notes will be provided.  
Literature   YunShik Lee, Principles of Terahertz Science and Technology, Springer 2009  Ali Rostami, Hassan Rasooli, and Hamed Baghban, Terahertz Technology: Fundamentals and Applications, Springer 2010  
Prerequisites / Notice  Basic foundation in physics, particularly, electromagnetics is required. Students who want to refresh their electromagnetics fundamentals can get additional material required for the course.  
252022000L  Introduction to Machine Learning Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact Link  W  8 credits  4V + 2U + 1A  A. Krause, F. Yang  
Abstract  The course introduces the foundations of learning and making predictions based on data.  
Objective  The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide handson experience in a course project.  
Content   Linear regression (overfitting, crossvalidation/bootstrap, model selection, regularization, [stochastic] gradient descent)  Linear classification: Logistic regression (feature selection, sparsity, multiclass)  Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); knearest neighbor  Neural networks (backpropagation, regularization, convolutional neural networks)  Unsupervised learning (kmeans, PCA, neural network autoencoders)  The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference)  Statistical decision theory (decision making based on statistical models and utility functions)  Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions)  Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE)  Bayesian approaches to unsupervised learning (Gaussian mixtures, EM)  
Prerequisites / Notice  Designed to provide a basis for following courses:  Advanced Machine Learning  Deep Learning  Probabilistic Artificial Intelligence  Seminar "Advanced Topics in Machine Learning"  
Competencies 
 
327213900L  Diffraction Physics in Materials Science  W  3 credits  3G  R. Erni  
Abstract  The lecture focuses on diffraction and scattering phenomena in materials science beyond basic Bragg diffraction. Introducing the Born approximation and Kirchhoff’s theory, diffraction from ideal and nonideal crystals is treated including, e.g., temperature and size effects, ordering phenomena, smallangle scattering and dynamical diffraction theories for both electron and Xray diffraction.  
Objective  • To become familiar with advanced diffraction phenomena in order to be able to explore the structure and properties of (solid) matter and their defects. • To be able to judge what type of diffraction method is suitable to probe what type of materials information. • To build up a generally applicable and fundamental theoretical understanding of scattering and diffraction effects. • To be able to identify limitations of the methods and the underlying theory which is commonly used to analyze diffraction data.  
Content  The course provides a general introduction to advanced diffraction phenomena in materials science. The lecture series covers the following topics: derivation of a general scattering theory based on Green’s function as basis for the introduction of the firstorder Born approximation; Kirchhoff’s diffraction theory with its integral theorem and the specific cases of Fresnel and Fraunhofer diffraction; diffraction from ideal crystals and diffraction from real crystals considering temperature effects expressed by the temperature DebyeWaller factor and by thermal diffuse scattering, atomic size effects expressed by the static DebyeWaller factor and diffuse scattering due to the modulation of the Laue monotonic scattering as a consequence of local order or clustering; the basics of smallangle scattering; and finally approaches used to treat dynamical diffraction are introduced. In addition, the specifics of Xray, electron and neutron scattering are being discussed. The course is complemented by a lab visit, selected exercises and short topical presentations given by the participants.  
Lecture notes  Fulltext script is available covering within about 100 pages the core topics of the lecture and all necessary derivations.  
Literature   Diffraction Physics, 3rd ed., J. M. Cowley, Elsevier, 1994.  XRay Diffraction, B. E. Warren, Dover, 1990.  Diffraction from Materials, 2nd ed., L. H. Schwartz, J. B. Cohen, Springer, 1987.  XRay Diffraction – In Crystals, Imperfect Crystals and Amorphous Bodies, A. Guinier, Dover, 1994.  Aberrationcorrected imaging in transmission electron microscopy, 2nd ed., R. Erni, Imperial College Press, 2015.  
Prerequisites / Notice  Basics of crystallography and the concept of reciprocal space, basics of electromagnetic and particle waves (but not mandatory)  
327214100L  Materials+  W  6 credits  6G  H. Galinski, R. Nicolosi Libanori  
Abstract  Materials+ is a teambased learning course focusing on sustained learning of key material concepts. This course teaches critical thinking and solving hands on material problems. The students will work in groups of five to solve a materials challenge. The eight weeklong project includes a poster presentation and culminates in a materials challenge, where all groups compete against each other.  
Objective  The overarching goal of this course is to provide students a riskfriendly environment, where they can learn the tools and mindset to aim for scientific breakthroughs. The materials challenge is thought to be a stimulus rather than a goal, to aim for new solutions and creative ideas. Students enrolled in the course will acquire technical skills on materials selection, integration and engineering. Furthermore, they will develop personal and social competencies, especially in decisionmaking, communication, cooperation, coordination, adaptability and flexibility, creative and critical thinking, project management, problemsolving, integrity and ethics.  
Content  In each term, the students will solve a materials challenge in class by applying three "stateoftheart" material science concepts. Students will take an active role as they work with their peers in small groups to strengthen and apply their learned expert skills. The course is designed to promote student learning of key material concepts in an applied context and stimulate the developing of soft skills from inter and intrateam social interactions.  
364057600L  Advanced Sustainability Economics PhD course, open for MSc students  W  3 credits  3G  E. Komarov, C. Renoir  
Abstract  The course covers current resource and sustainability economics, including ethical foundations of sustainability, intertemporal optimisation in capitalresource economies, sustainable use of nonrenewable and renewable resources, pollution dynamics, population growth, and sectoral heterogeneity. A final part is on empirical contributions, e.g. the resource curse, energy prices, and the EKC.  
Objective  Understanding of the current issues and economic methods in sustainability research; ability to solve typical problems like the calculation of the growth rate under environmental restriction with the help of appropriate model equations. Please note that the course takes places in Zurichbergstrasse 18, which requires an ETH card to enter. We kindly ask NonETH students to inform Clément Renoir if they would like to attend.  
529044200L  Advanced Kinetics  W  6 credits  3G  J. Richardson  
Abstract  This lecture covers the theoretical and conceptual foundations of quantum dynamics in molecular systems. Particular attention is taken to derive and compare quantum and classical approximations which can be used to simulate the dynamics of molecular systems and the reaction rate constant used in chemical kinetics.  
Objective  The theory of quantum dynamics is derived from the timedependent Schrödinger equation. This is illustrated with molecular examples including tunnelling, recurrences, nonadiabatic crossings. We consider thermal distributions, correlation functions, interaction with light and nonadiabatic effects. Quantum scattering theory is introduced and applied to discuss molecular collisions. The dynamics of systems with a very large number of quantum states are discussed to understand the transition from microscopic to macroscopic dynamics. A rigorous rate theory is obtained both from a quantummechanical picture as well as within the classical approximation. The approximations leading to conventional transitionstate theory for polyatomic reactions are discussed. In this way, relaxation and irreversibility will be explained which are at the foundation of statistical mechanics. By the end of the course, the student will have learned many ways to simplify the complex problem posed by quantum dynamics. They will understand when and why certain approximations are valid in different situations and will use this to make quantitative and qualitative predictions about how different molecular systems behave.  
Lecture notes  Will be available online.  
Literature  D. J. Tannor, Introduction to Quantum Mechanics: A TimeDependent Perspective R. D. Levine, Molecular Reaction Dynamics S. Mukamel, Principles of Nonlinear Optical Spectroscopy  
Prerequisites / Notice  529042200L Physical Chemistry II: Chemical Reaction Dynamics  
529043400L  Physical Chemistry V: Spectroscopy  W  4 credits  3G  H. J. Wörner  
Abstract  thermal radiation and Planck's law; transition probabilities, rate equations; atomic structure and spectra electronic, vibrational, and rotational spectroscopy of molecules symmetry, group theory, and selection rules  
Objective  When you successfully finished this course, you are able to analyze and interpret electronic spectra of atoms and rotational, vibrational as well as electronic spectra of molecules. In particular, you will be able * to determine the term symbols of the states of atoms, as well as diatomic and polyatomic molecules * to explain the theoretical steps that are needed to separate the motions of nuclei and electrons (BornOppenheimer approximation) as well as rotations and vibrations of the nuclear motion (normalmode approximation), * to use group theory as tool in spectroscopy, e.g. to classify rotational modes according to symmetry and predict their spectroscopic activity, to construct symmetryadapted molecular orbitals, and to use the symmetry of states to derive selection rules of molecules, * to use a quantummechanical picture to explain intensities of vibrational progressions of an electronic spectrum (FranckCondon factors), and * to determine selection rules for spectroscopic transitions based on a qualitative evaluation of the dipole matrix element.  
Content  Basics: thermal radiation, Planck's law transition probabilities rate equations Einstein coefficients and lasers Atomic and molecular spectroscopy: tools to evaluate the transition matrix elements which describe atomic and molecular spectra quantummechanically, in particular  selection rules and symmetry/group theory : separation of electrons and nuclei (BornOppenheimer approximation)  separation of vibrations and rotations (normal mode approximation) and how to use these tools to understand and predict spectra qualitatively  
Lecture notes  is available on the lecture website  
529044000L  Physical Electrochemistry and Electrocatalysis  W  6 credits  3G  T. Schmidt  
Abstract  Fundamentals of electrochemistry, electrochemical electron transfer, electrochemical processes, electrochemical kinetics, electrocatalysis, surface electrochemistry, electrochemical energy conversion processes and introduction into the technologies (e.g., fuel cell, electrolysis), electrochemical methods (e.g., voltammetry, impedance spectroscopy), mass transport.  
Objective  Providing an overview and indepth understanding of Fundamentals of electrochemistry, electrochemical electron transfer, electrochemical processes, electrochemical kinetics, electrocatalysis, surface electrochemistry, electrochemical energy conversion processes (fuel cell, electrolysis), electrochemical methods and mass transport during electrochemical reactions. The students will learn about the importance of electrochemical kinetics and its relation to industrial electrochemical processes and in the energy seactor.  
Content  Review of electrochemical thermodynamics, description electrochemical kinetics, ButlerVolmer equation, Tafel kinetics, simple electrochemical reactions, electron transfer, Marcus Theory, fundamentals of electrocatalysis, elementary reaction processes, ratedetermining steps in electrochemical reactions, practical examples and applications specifically for electrochemical energy conversion processes, introduction to electrochemical methods, mass transport in electrochemical systems. Introduction to fuel cells and electrolysis  
Lecture notes  Will be handed out during the Semester  
Literature  Physical Electrochemistry, E. Gileadi, Wiley VCH Electrochemical Methods, A. Bard/L. Faulkner, WileyVCH Modern Electrochemistry 2A  Fundamentals of Electrodics, J. Bockris, A. Reddy, M. GamboaAldeco, Kluwer Academic/Plenum Publishers  
227094800L  Magnetic Resonance Imaging in Medicine  W  4 credits  3G  S. Kozerke, M. Weiger Senften  
Abstract  Introduction to magnetic resonance imaging and spectroscopy, encoding and contrast mechanisms and their application in medicine.  
Objective  Understand the basic principles of signal generation, image encoding and decoding, contrast manipulation and the application thereof to assess anatomical and functional information invivo.  
Content  Introduction to magnetic resonance imaging including basic phenomena of nuclear magnetic resonance; 2 and 3dimensional imaging procedures; fast and parallel imaging techniques; image reconstruction; pulse sequences and image contrast manipulation; equipment; advanced techniques for identifying activated brain areas; perfusion and flow; diffusion tensor imaging and fiber tracking; contrast agents; localized magnetic resonance spectroscopy and spectroscopic imaging; diagnostic applications and applications in research.  
Lecture notes  D. Meier, P. Boesiger, S. Kozerke Magnetic Resonance Imaging and Spectroscopy  
227030300L  Advanced Photonics  W  6 credits  2V + 2U + 1A  A. Emboras, M. Csontos, A. Dorodnyy  
Abstract  The lecture gives a comprehensive insight into various types of nanoscale photonic devices, physical fundamentals of their operation, and an overview of the micro/nanofabrication technologies. Following applications of nanoscale photonic structures are discussed in details: detectors, photovoltaic cells, atomic/ionic optoelectronic devices and integrated microwave photonics.  
Objective  General training in advanced photonic devices with an indepth understanding of the fundamentals of theory, fabrication, and characterization. Handson experience with photonic and optoelectronic device technologies and theory. The students will learn about the importance of advanced photonic devices in energy, communications, digital and neuromorphic computing applications.  
Content  The following topics will be addressed:  Photovoltaics: basic thermodynamic principles and fundamental efficiency limitations, physics of semiconductor solar cell, overview of existing solar cell concepts and underlying physical phenomena.  Micro/nanofabrication technologies for advanced optoelectronic devices: introduction and device examples.  Comprehensive insight into the physical mechanisms that govern ionicatomic devices, present the techniques required to fabricate ultrascaled nanostructures and show some applications in digital and neuromorphic computing.  Revealing quantum mechanical effects in atomic to nanometer sized conductors. Understanding how their dynamics can be utilized in hardware based artificial neural networks.  Introduction to microwave photonics (MWP), microwave photonic links, photonic techniques for microwave signal generation and processing.  
Lecture notes  The presentation and the lecture notes will be provided every week.  
Literature  “Atomic/Ionic Devices”: • Resistive Switching: From Fundamentals of Nanoionic Redox Processes to Memristive Device Applications, Daniele Ielmini and Rainer Waser, WileyVCH • Electrochemical Methods: Fundamentals and Applications, A. Bard and L. Faulkner, John Willey & Sons, Inc. • Molecular Electronics: An Introduction to Theory and Experiment, Elke Scheer and Juan Carlos Cuevas “Photovoltaics”: • Prof. Peter Wurfel: Physics of Solar Cells, Wiley “Micro and nano Fabrication”: • Prof. H. Gatzen, Prof. Volker Saile, Prof. Juerg Leuthold: Micro and Nano Fabrication, Springer “Microwave Photonics”: • D. M. Pozar, Microwave Engineering. J. Wiley & Sons, New York, 2005. • M. Burla, Advanced integrated optical beam forming networks for broadband phased array antenna systems. Enschede, The Netherlands, 2013. DOI: 10.3990/1.9789036507295 • C.H. Cox, Analog optical links: theory and practice. Cambridge University Press, 2006.  
Prerequisites / Notice  Basic knowledge of semiconductor physics, physics of the electromagnetic filed and thermodynamics.  
Competencies 
 
227039000L  Elements of Microscopy  W  4 credits  3G  M. Stampanoni, G. Csúcs, A. Sologubenko  
Abstract  The lecture reviews the basics of microscopy by discussing wave propagation, diffraction phenomena and aberrations. It gives the basics of light microscopy, introducing fluorescence, widefield, confocal and multiphoton imaging. It further covers 3D electron microscopy and 3D Xray tomographic micro and nanoimaging.  
Objective  Solid introduction to the basics of microscopy, either with visible light, electrons or Xrays.  
Content  It would be impossible to imagine any scientific activities without the help of microscopy. Nowadays, scientists can count on very powerful instruments that allow investigating sample down to the atomic level. The lecture includes a general introduction to the principles of microscopy, from wave physics to image formation. It provides the physical and engineering basics to understand visible light, electron and Xray microscopy. During selected exercises in the lab, several sophisticated instrument will be explained and their capabilities demonstrated.  
Literature  Available Online.  
227039600L  EXCITE Interdisciplinary Summer School on BioMedical Imaging The school admits 60 MSc or PhD students with backgrounds in biology, chemistry, mathematics, physics, computer science or engineering based on a selection process. Students have to apply for acceptance. To apply a curriculum vitae and an application letter need to be submitted. Further information can be found at: Link.  W  4 credits  6G  S. Kozerke, B. Menze, M. P. Wolf, U. Ziegler Lang  
Abstract  Twoweek summer school organized by EXCITE (Center for EXperimental & Clinical Imaging TEchnologies Zurich) on biological and medical imaging. The course covers Xray imaging, magnetic resonance imaging, nuclear imaging, ultrasound imaging, optoacoustic imaging, infrared and optical microscopy, electron microscopy, image processing and analysis.  
Objective  Students understand basic concepts and implementations of biological and medical imaging. Based on relative advantages and limitations of each method they can identify preferred procedures and applications. Common foundations and conceptual differences of the methods can be explained.  
Content  Twoweek summer school on biological and medical imaging. The course covers concepts and implementations of Xray imaging, magnetic resonance imaging, nuclear imaging, ultrasound imaging, optoacoustic imaging, infrared and optical microscopy and electron microscopy. Multimodal and multiscale imaging and supporting technologies such as image analysis and modeling are discussed. Dedicated modules for physical and life scientists taking into account the various backgrounds are offered.  
Lecture notes  Presentation slides, Web links  
Prerequisites / Notice  The school admits 60 MSc or PhD students with backgrounds in biology, chemistry, mathematics, physics, computer science or engineering based on a selection process. To apply a curriculum vitae, a statement of purpose and applicants references need to be submitted. Further information can be found at: Link  
227043200L  Learning, Classification and Compression  W  4 credits  2V + 1U  E. Riegler  
Abstract  The focus of the course is aligned to a theoretical approach of learning theory and classification and an introduction to lossy and lossless compression for general sets and measures. We will mainly focus on a probabilistic approach, where an underlying distribution must be learned/compressed. The concepts acquired in the course are of broad and general interest in data sciences.  
Objective  After attending this lecture and participating in the exercise sessions, students will have acquired a working knowledge of learning theory, classification, and compression.  
Content  1. Learning Theory (a) Framework of Learning (b) Hypothesis Spaces and Target Functions (c) Reproducing Kernel Hilbert Spaces (d) BiasVariance Tradeoff (e) Estimation of Sample and Approximation Error 2. Classification (a) Binary Classifier (b) Support Vector Machines (separable case) (c) Support Vector Machines (nonseparable case) (d) Kernel Trick 3. Lossy and Lossless Compression (a) Basics of Compression (b) Compressed Sensing for General Sets and Measures (c) Quantization and Rate Distortion Theory for General Sets and Measures  
Lecture notes  Detailed lecture notes will be provided.  
Prerequisites / Notice  This course is aimed at students with a solid background in measure theory and linear algebra and basic knowledge in functional analysis.  
227043410L  Mathematics of Information  W  8 credits  3V + 2U + 2A  H. Bölcskei  
Abstract  The class focuses on mathematical aspects of 1. Information science: Sampling theorems, frame theory, compressed sensing, sparsity, superresolution, spectrumblind sampling, subspace algorithms, dimensionality reduction 2. Learning theory: Approximation theory, greedy algorithms, uniform laws of large numbers, Rademacher complexity, VapnikChervonenkis dimension  
Objective  The aim of the class is to familiarize the students with the most commonly used mathematical theories in data science, highdimensional data analysis, and learning theory. The class consists of the lecture and exercise sessions with homework problems.  
Content  Mathematics of Information 1. Signal representations: Frame theory, wavelets, Gabor expansions, sampling theorems, density theorems 2. Sparsity and compressed sensing: Sparse linear models, uncertainty relations in sparse signal recovery, superresolution, spectrumblind sampling, subspace algorithms (ESPRIT), estimation in the highdimensional noisy case, Lasso 3. Dimensionality reduction: Random projections, the JohnsonLindenstrauss Lemma Mathematics of Learning 4. Approximation theory: Nonlinear approximation theory, best Mterm approximation, greedy algorithms, fundamental limits on compressibility of signal classes, KolmogorovTikhomirov epsilonentropy of signal classes, optimal compression of signal classes 5. Uniform laws of large numbers: Rademacher complexity, VapnikChervonenkis dimension, classes with polynomial discrimination  
Lecture notes  Detailed lecture notes will be provided at the beginning of the semester.  
Prerequisites / Notice  This course is aimed at students with a background in basic linear algebra, analysis, statistics, and probability. We encourage students who are interested in mathematical data science to take both this course and "401494420L Mathematics of Data Science" by Prof. A. Bandeira. The two courses are designed to be complementary. H. Bölcskei and A. Bandeira  
227015900L  Semiconductor Devices: Quantum Transport at the Nanoscale  W  6 credits  2V + 2U  M. Luisier, A. Emboras  
Abstract  This class offers an introduction into quantum transport theory, a rigorous approach to electron transport at the nanoscale. It covers different topics such as bandstructure, Wave Function and Nonequilibrium Green's Function formalisms, and electron interactions with their environment. Matlab exercises accompany the lectures where students learn how to develop their own transport simulator.  
Objective  The continuous scaling of electronic devices has given rise to structures whose dimensions do not exceed a few atomic layers. At this size, electrons do not behave as particle any more, but as propagating waves and the classical representation of electron transport as the sum of driftdiffusion processes fails. The purpose of this class is to explore and understand the displacement of electrons through nanoscale device structures based on stateoftheart quantum transport methods and to get familiar with the underlying equations by developing his own nanoelectronic device simulator.  
Content  The following topics will be addressed:  Introduction to quantum transport modeling  Bandstructure representation and effective mass approximation  Open vs closed boundary conditions to the Schrödinger equation  Comparison of the Wave Function and Nonequilibrium Green's Function formalisms as solution to the Schrödinger equation  Selfconsistent SchödingerPoisson simulations  Quantum transport simulations of resonant tunneling diodes and quantum well nanotransistors  Topofthebarrier simulation approach to nanotransistor  Electron interactions with their environment (phonon, roughness, impurity,...)  Multiband transport models  
Lecture notes  Lecture slides are distributed every week and can be found at Link  
Literature  Recommended textbook: "Electronic Transport in Mesoscopic Systems", Supriyo Datta, Cambridge Studies in Semiconductor Physics and Microelectronic Engineering, 1997  
Prerequisites / Notice  Basic knowledge of semiconductor device physics and quantum mechanics  
Competencies 
 
227039500L  Neural Systems  W  6 credits  2V + 1U + 1A  R. Hahnloser, M. F. Yanik, B. Grewe  
Abstract  This course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experiment techniques used in animal research and methods for inferring neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on artificially intelligent systems.  
Objective  This course introduces  Basic neurophysiology and mathematical descriptions of neurons  Methods for dissecting animal behavior  Neural recordings in intact nervous systems and information decoding principles  Methods for manipulating the state and activity in selective neuron types  Neuromodulatory systems and their computational roles  Reward circuits and reinforcement learning  Imaging methods for reconstructing the synaptic networks among neurons  Birdsong and language  Neurobiological principles for machine learning.  
Content  From active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics.  
Prerequisites / Notice  Before taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227039310L). As part of the exercises for this class, students are expected to complete a programming or literature review project to be defined at the beginning of the semester.  
363058800L  Complex Networks  W  4 credits  2V + 1U  G. Casiraghi  
Abstract  The course provides an overview of the methods and abstractions used in (i) the quantitative study of complex networks, (ii) empirical network analysis, (iii) the study of dynamical processes in networked systems, (iv) the analysis of robustness of networked systems, (v) the study of network evolution, and (vi) data mining techniques for networked data sets.  
Objective  * the network approach to complex systems, where actors are represented as nodes and interactions are represented as links * learn about structural properties of classes of networks * learn about feedback mechanism in the formation of networks * learn about statistical inference and data mining techniques for data on networked systems * learn methods and abstractions used in the growing literature on complex networks  
Content  Networks matter! This holds for social and economic systems, for technical infrastructures as well as for information systems. Increasingly, these networked systems are outside the control of a centralized authority but rather evolve in a distributed and selforganized way. How can we understand their evolution and what are the local processes that shape their global features? How does their topology influence dynamical processes like diffusion? And how can we characterize the importance of specific nodes? This course provides a systematic answer to such questions, by developing methods and tools which can be applied to networks in diverse areas like infrastructure, communication, information systems, biology or (online) social networks. In a network approach, agents in such systems (like e.g. humans, computers, documents, power plants, biological or financial entities) are represented as nodes, whereas their interactions are represented as links. The first part of the course, "Introduction to networks: basic and advanced metrics", describes how networks can be represented mathematically and how the properties of their link structures can be quantified empirically. In a second part "Stochastic Models of Complex Networks" we address how analytical statements about crucial properties like connectedness or robustness can be made based on simple macroscopic stochastic models without knowing the details of a topology. In the third part we address "Dynamical processes on complex networks". We show how a simple model for a random walk in networks can give insights into the authority of nodes, the efficiency of diffusion processes as well as the existence of community structures. A fourth part "Network Optimisation and Inference" introduces models for the emergence of complex topological features which are due to stochastic optimization processes, as well as statistical methods to detect patterns in large data sets on networks. In a fifth part, we address "Network Dynamics", introducing models for the emergence of complex features that are due to (i) feedback phenomena in simple network growth processes or (iii) order correlations in systems with highly dynamic links. A final part "Research Trends" introduces recent research on the application of data mining and machine learning techniques to relational data.  
Lecture notes  The lecture slides are provided as handouts  including notes and literature sources  to registered students only. All material is to be found on Moodle.  
Literature  See handouts. Specific literature is provided for download  for registered students, only.  
Prerequisites / Notice  There are no prerequisites for this course. Selfstudy tasks (to be solved analytically and by means of computer simulations) are provided as home work. Weekly exercises (45 min) are used to discuss selected solutions. Active participation in the exercises is strongly suggested for a successful completion of the final exam.  
Competencies 
 
363054300L  AgentBased Modelling of Social Systems  W  3 credits  2V + 1U  G. Vaccario  
Abstract  Agentbased modeling is introduced as a bottomup approach to understand the complex dynamics of social systems. The course is based on formal models of agents and their interactions. Computer simulations using Python allow the quantitative analysis of a wide range of social phenomena, e.g. cooperation and competition, opinion dynamics, spatial interactions and behaviour in social networks.  
Objective  A successful participant of this course is able to  understand the rationale of agentbased models of social systems  understand the relation between rules implemented at the individual level and the emerging behavior at the global level  learn to choose appropriate model classes to characterize different social systems  grasp the influence of agent heterogeneity on the model output  efficiently implement agentbased models using Python and visualize the output  
Content  This fullfeatured course on agentbased modeling (ABM) allows participants with no prior expertise to understand concepts, methods and tools of ABM, to apply them in their master or doctoral thesis. We focus on a formal description of agents and their interactions, to allow for a suitable implementation in computer simulations. Given certain rules for the agents, we are interested to model their collective dynamics on the systemic level. Agentbased modeling is introduced as a bottomup approach to understand the complex dynamics of social systems. Agents represent the basic constituents of such systems. The are described by internal states or degrees of freedom (opinions, strategies, etc.), the ability to perceive and change their environment, and the ability to interact with other agents. Their individual (microscopic) actions and interactions with other agents, result in macroscopic (collective, system) dynamics with emergent properties, which we want to understand and to analyze. The course is structured in three main parts. The first two parts introduce two main agent concepts  Boolean agents and Brownian agents, which differ in how the internal dynamics of agents is represented. Boolean agents are characterized by binary internal states, e.g. yes/no opinion, while Brownian agents can have a continuous spectrum of internal states, e.g. preferences and attitudes. The last part introduces models in which agents interact in physical space, e.g. migrate or move collectively. Throughout the course, we will discuss a wide variety of application areas, such as:  opinion dynamics and social influence,  cooperation and competition,  online social networks,  systemic risk  emotional influence and communication  swarming behavior  spatial competition While the lectures focus on the theoretical foundations of agentbased modeling, weekly exercise classes provide practical skills. Using the Python programming language, the participants implement agentbased models in guided and in selfchosen projects, which they present and jointly discuss.  
Lecture notes  The lecture slides will be available on the Moodle platform, for registered students only.  
Literature  See handouts. Specific literature is provided for download, for registered students only.  
Prerequisites / Notice  Participants of the course should have some background in mathematics and an interest in formal modeling and in computer simulations, and should be motivated to learn about social systems from a quantitative perspective. Prior knowledge of Python is not necessary. Selfstudy tasks are provided as home work for small teams (24 members). Weekly exercises (45 min) are used to discuss the solutions and guide the students. The examination will account for 70% of the grade and will be conducted electronically. The "closed book" rule applies: no books, no summaries, no lecture materials. The exam questions and answers will be only in English. The use of a paperbased dictionary is permitted. The group project to be handed in at the beginning of July will count 30% to the final grade.  
465095200L  Biomedical Photonics  W  3 credits  2V  M. Frenz  
Abstract  The lecture introduces the principles of light generation, light propagation in tissue and detection of light and its therapeutic and diagnostic application in medicine.  
Objective  The students are expected to aquire a basic understanding of the fundamental physical principles within biomedical photonics. In particular, they will develop a broad skill set for research in fundamentals of lighttissue interaction, technologies such as microscopy, lasers and fiber optics and issues related to light applications in therapeutics and diagnostics in medicine.  
Content  Optics always was strongly connected to the observation and interpretation of physiological phenomenon. The basic knowledge of optics for example was initially gained by studying the function of the human eye. Nowadays, biomedical optics is an independent research field that is no longer restricted to the observation of physiological processes but studies diagnostic and therapeutic problems in medicine. A basic prerequisite for applying optical techniques in medicine is the understanding of the physical properties of light, the light propagation in and its interaction with tissue. The lecture gives inside into the generation, propagation and detection of light, its propagation in tissue and into selected optical applications in medicine. Various optical imaging techniques (optical coherence tomography or optoacoustics) as well as therapeutic laser applications (refractive surgery, photodynamic therapy or nanosurgery) will be discussed.  
Lecture notes  will be provided via Internet (Ilias)  
Literature   M. Born, E. Wolf, "Principles of Optics", Pergamon Press  B.E.A. Saleh, M.C. Teich, "Fundamentals of Photonics", John Wiley and Sons, Inc.  O. Svelto, "Principles of Lasers", Plenum Press  J. Eichler, T. Seiler, "Lasertechnik in der Medizin", Springer Verlag  M.H. Niemz, "LaserTissue Interaction", Springer Verlag  A.J. Welch, M.J.C. van Gemert, "Opticalthermal response of laserirradiated tissue", Plenum Press  
Prerequisites / Notice  Language of instruction: English This is the same course unit (465095200L) with former course title "Medical Optics".  
701170800L  Infectious Disease Dynamics  W  4 credits  2V  R. R. Regös, S. Bonhoeffer, R. D. Kouyos, T. Stadler  
Abstract  This course introduces into current research on the population biology of infectious diseases. The course discusses the most important mathematical tools and their application to relevant diseases of human, natural or managed populations.  
Objective  Attendees will learn about: * the impact of important infectious pathogens and their evolution on human, natural and managed populations * the population biological impact of interventions such as treatment or vaccination * the impact of population structure on disease transmission Attendees will learn how: * the emergence spread of infectious diseases is described mathematically * the impact of interventions can be predicted and optimized with mathematical models * population biological models are parameterized from empirical data * genetic information can be used to infer the population biology of the infectious disease The course will focus on how the formal methods ("how") can be used to derive biological insights about the hostpathogen system ("about").  
Content  After an introduction into the history of infectious diseases and epidemiology the course will discuss basic epidemiological models and the mathematical methods of their analysis. We will then discuss the population dynamical effects of intervention strategies such as vaccination and treatment. In the second part of the course we will introduce into more advanced topics such as the effect of spatial population structure, explicit contact structure, host heterogeneity, and stochasticity. In the final part of the course we will introduce basic concepts of phylogenetic analysis in the context of infectious diseases.  
Lecture notes  Slides and script of the lecture will be available online.  
Literature  The course is not based on any of the textbooks below, but they are excellent choices as accompanying material: * Keeling & Rohani, Modeling Infectious Diseases in Humans and Animals, Princeton Univ Press 2008 * Anderson & May, Infectious Diseases in Humans, Oxford Univ Press 1990 * Murray, Mathematical Biology, Springer 2002/3 * Nowak & May, Virus Dynamics, Oxford Univ Press 2000 * Holmes, The Evolution and Emergence of RNA Viruses, Oxford Univ Press 2009  
Prerequisites / Notice  Basic knowledge of population dynamics and population genetics as well as linear algebra and analysis will be an advantage.  
Competencies 
 
701123600L  Measurement Methods in Meteorology and Climate Research  W  1 credit  1V  M. Hirschi, D. Michel  
Abstract  The course provides the physical, technical, and theoretical basics for measuring physical quantities in the atmosphere. Also, considerations related to the planning of observation campaigns and to data evaluation are discussed.  
Objective  Aims of the course are:  to become sensitive for specific problems when making measurements in the atmosphere under severe environmental conditions  to gain knowledge of the different measurement methods and techniques  to develop criteria for the choice of the optimal measurement method for a given problem  to find the optimal observation strategy in terms of choice of instrument, frequency of observation, accuracy, etc.  
Content  Problems related to time series analysis, sampling theorem, time constant and sampling rate. Theoretical analysis of different sensors for temperature, humidity, wind, and pressure. Discussion of effects disturbing the instruments. Principles of active and passive remote sensing. Measuring turbulent fluxes (e.g. heatflux) using eddycorrelation technique. Discussion of technical realizations of complex observing systems (radiosondes, automatic weather stations, radar, wind profilers). Demonstration of instruments.  
Lecture notes  Students can download a copy of the lectures as PDFfiles.  
Literature   Emeis, Stefan: Measurement Methods in Atmospheric Sciences, In situ and remote. Bornträger 2010, ISBN 9783443010669  Brock, F. V. and S. J. Richardson: Meteorological Measurement Systems, Oxford University Press 2001, ISBN 0195134516  Thomas P. DeFelice: An Introduction to Meteorological Instrumentation and Measurement. PrenticeHall 2000, 229 p., ISBN 0132432706  Fritschen, L.J., Gay L.W.: Environmental Instrumentation, 216 p., Springer, New York 1979.  Lenschow, D.H. (ed.): Probing the Atmospheric Boundary Layer, 269 p., American Meteorological Society, Boston MA 1986.  Meteorological Office (publ.): Handbook of Meteorological Instruments, 8 vols., Her Majesty's Stationery Office, London 1980.  Wang, J.Y., Felton, C.M.M.: Instruments for Physical Environmental measurements, 2 vol., 801 p., Kendall/Hunt Publ. Comp., Dubuque Iowa 1975/76.  
Prerequisites / Notice  The lecture focuses on physical atmospheric parameters while lecture 701023400 concentrates on the chemical quantities. The lectures are complementary, together they provide the instrumental basics for the lab course 701046000. Contact hours of the lab course are such that the lectures can be attended (which is recommended).  
701023400L  Atmospheric Chemistry: Instruments and Measuring Techniques  W  1 credit  1V  U. Krieger  
Abstract  Measuring Techniques: Environmental Monitoring, Trace Gas Detection, Remote Sensing, Aerosol Characterization, Techniques used in the laboratory.  
Objective  Find out about the specific problems connected to composition measurements in the atmosphere. Working out criteria for selecting an optimal measuring strategy. Acquiring knowledge about different measuring methods their spectroscopic principles and of some specific instruments.  
Content  Es werden Methoden und Geräte vorgestellt und theoretisch analysiert, die in atmosphärenchemischen Messungen Verwendung finden: Geräte zur Überwachung im Rahmen der Luftreinhalteverordnung, Spurengasanlysemethoden, "remote sensing", Aerosolmessgeräte, Messverfahren bei Labormessungen zu atmosphärischen Fragestellungen.  
Literature  B. J. FinnlaysonPitts, J. N. Pitts, "Chemistry of the Upper and Lower Atmosphere", Academic Press, San Diego, 2000  
Prerequisites / Notice  Methodenvorlesung zu den Praktika 701046000 und 701123000. Die Kontaktzeiten in diesen Praktika sind so abgestimmt, dass der (empfohlene) Besuch der Vorlesung möglich ist. Voraussetzungen: Atmosphärenphysik I und II  
701041200L  Climate Systems  W  3 credits  2G  L. Gudmundsson, D. Schumacher  
Abstract  This course introduces the most important physical components of the climate system and their interactions. The mechanisms of anthropogenic climate change are analysed against the background of climate history and variability. Those completing the course will be in a position to identify and explain simple problems in the area of climate systems.  
Objective  Students are able  to describe the most important physical components of the global climate system and sketch their interactions  to explain the mechanisms of anthropogenic climate change  to identify and explain simple problems in the area of climate systems  
Lecture notes  Copies of the slides are provided in electronic form.  
Literature  A comprehensive list of references is provided in the class. Two books are particularly recommended:  Hartmann, D., 2016: Global Physical Climatology. Academic Press, London, 485 pp.  Peixoto, J.P. and A.H. Oort, 1992: Physics of Climate. American Institute of Physics, New York, 520 pp.  
Prerequisites / Notice  Teaching: Lukas Gudmundsson & Dominik Schumacher, several keynotes to special topics by other professors Course taught in german/english, slides in english  
701125200L  Climate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation  W  3 credits  2V + 1U  D. N. Bresch, R. Knutti  
Abstract  The course introduces the concepts of predictability, probability, uncertainty and probabilistic risk modelling and their application to climate modeling and the economics of climate adaptation.  
Objective  Students will acquire knowledge in uncertainty and risk quantification (probabilistic modelling) and an understanding of the economics of climate adaptation. They will become able to construct their own uncertainty and risk assessment models (in Python), hence basic understanding of scientific programming forms a prerequisite of the course.  
Content  The first part of the course covers methods to quantify uncertainty in detecting and attributing human influence on climate change and to generate probabilistic climate change projections on global to regional scales. Model evaluation, calibration and structural error are discussed. In the second part, quantification of risks associated with local climate impacts and the economics of different baskets of climate adaptation options are assessed – leading to informed decisions to optimally allocate resources. Such preemptive risk management allows evaluating a mix of prevention, preparation, response, recovery, and (financial) risk transfer actions, resulting in an optimal balance of public and private contributions to risk management, aiming at a more resilient society. The course provides an introduction to the following themes: 1) basics of probabilistic modelling and quantification of uncertainty from global climate change to local impacts of extreme events 2) methods to optimize and constrain model parameters using observations 3) risk management from identification (perception) and understanding (assessment, modelling) to actions (prevention, preparation, response, recovery, risk transfer) 4) basics of economic evaluation, economic decision making in the presence of climate risks and preemptive risk management to optimally allocate resources  
Lecture notes  Powerpoint slides will be made available.  
Literature  Many papers for indepth study will be referred to during the lecture. For the exercises the CLIMADA platform Link  will be (extensively) used.  
Prerequisites / Notice  Handson experience with probabilistic climate models and risk models will be acquired in the tutorials; hence good understanding of scientific programming forms a prerequisite of the course, in Python (teaching language, object oriented) or similar. Basic understanding of the climate system, e.g. as covered in the course 'Klimasysteme' is required. Examination: graded tutorials during the semester (benotete Semesterleistung)  
Competencies 
 
851065500L  ETH Global Development Summer School  W  3 credits  6G  A. Rom, K. W. Axhausen, P. Krütli, M. Makridis, M. Mertens  
Abstract  The ETH Global Development Summer School provides young researchers with the opportunity to work on current and sustainabilityrelated topics in interdisciplinary and intercultural teams. Focus is given not only to teaching theoretical knowledge but also to solving specific case studies.  
Objective  Within ETH Zurich's Critical Thinking Initiative (CTI), students further develop their critical thinking and communications skills including: the capability to analyse and reflect critically, to form an independent opinion and develop a point of view, as well as to communicate, argue and act in an effective and responsible manner. Based on this concept, the ETH Global Development Summer School is providing its students with the following qualifications and learning outcomes:  Interdisciplinary and multicultural competence: Students gain basic knowledge in scientific disciplines beyond their own and learn how to work effectively in interdisciplinary and multicultural teams.  Methodological competence: Students gain basic knowledge of different scientific methods beyond their selected study discipline.  Reflection competence: Students learn to critically reflect their own way of thinking, their own research approaches, and how academia influences and interacts with society at large.  Implementation skills: Students will apply creative technologies in solution finding processes to gain knowledge and prototypingskills to increase handson experience by applying knowledge in concrete cases. This year's event on sustainable mobility is a collaboration between ETH for Development (ETH4D) and Kwame Nkrumah University of Science and Technology (KNUST, Kumasi, Ghana), and will take place in Kumasi, Ghana. To find more information and to register, visit our website: Link  
Content  The Summer School 2023 is a collaboration between ETH for Development (ETH4D) and Kwame Nkrumah University of Science & Technology (KNUST) in Kumasi, Ghana. It provides students and young researchers the opportunity to develop and test solutions for a realworld challenge related to mobility. Students will work in interdisciplinary teams. The summer school will be held in person in Kumasi, Ghana.  
Literature  further information and registration: Link  
Prerequisites / Notice  No prerequisites. The summer school is open to Bachelor, Master and Doctoral students from all disciplines. Candidates must apply for the limited slots through a competitive application process that is open until 6 March 2023 at Link. Applications will be evaluated on their academic strength, creativity, technicalrelated expertise, and their dedication to contribute to solving the world's most pressing challenges. Participants will be informed of the selection by 10 March 2023. Depending on the Covid19 situation, the course might have to change format or be postponed.  
Competencies 
