Search result: Catalogue data in Spring Semester 2021

Physics Master Information
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 ( after having received the credits.)
227-1046-00LComputer Simulations of Sensory Systems Information W3 credits3GT. Haslwanter
AbstractThis 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 neuro-sensory prostheses.
ObjectiveOur 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!!
ContentThe following topics will be covered:
• Introduction into the signal processing in nerve cells.
• Introduction into Python.
• Simplified simulation of nerve cells (Hodgkins-Huxley 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 notesFor each module additional material will be provided on the e-learning platform "moodle". The main content of the lecture is also available as a wikibook, under
LiteratureOpen source information is available as wikibook

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: 978-1-84169-698-0 (hardcover), oder 978-1-84169-699-7 (paperback)]
A coherent, up-to-date introduction to the basic facts and theories concerning human sensory perception.

• The best place to get started with Python programming are the

On signal processing with Python, my upcoming book
• Hands-on Signal Analysis with Python (Due: January 13, 2021
ISBN 978-3-030-57902-9,
will contain an explanation to all the required programming tools and packages.
Prerequisites / Notice• Since I have to gravel from Linz, Austria, to Zurich to give this lecture, I plan to hold this lecture in blocks (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.
465-0952-00LBiomedical PhotonicsW3 credits2VM. Frenz
AbstractThe lecture introduces the principles of light generation, light propagation in tissue and detection of light and its therapeutic and diagnostic application in medicine.
ObjectiveThe 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 light-tissue interaction, technologies such as microscopy, lasers and fiber optics and issues related to light applications in therapeutics and diagnostics in medicine.
ContentOptics 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 noteswill 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, "Laser-Tissue Interaction", Springer Verlag
- A.J. Welch, M.J.C. van Gemert, "Optical-thermal response of laser-irradiated tissue", Plenum Press
Prerequisites / NoticeLanguage of instruction: English
This is the same course unit (465-0952-00L) with former course title "Medical Optics".
151-0160-00LNuclear Energy SystemsW4 credits2V + 1UH.‑M. Prasser, P. Burgherr, I. Günther-Leopold, W. Hummel, T. Kämpfer, T. Kober, X. Zhang
AbstractNuclear energy and sustainability, uranium production, uranium enrichment, nuclear fuel production, reprocessing of spent fuel, nuclear waste disposal, Life Cycle Analysis, energy and materials balance of Nuclear Power Plants.
ObjectiveStudents 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. The are enabled to assess to potentials and risks arising from embedding nuclear power in a complex energy system.
Content(1) survey on the cosmic and geological origin of uranium, methods of uranium mining, separation of uranium from the ore, (2) enrichment of uranium (diffusion cells, ultra-centrifuges, alternative methods), chemical conversion uranium oxid - fluorid - oxid, fuel rod fabrication processes, (3) 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. (4) 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, (5) methods to measure the sustainability of energy systems, comparison of nuclear energy with other energy sources, environmental impact of the nuclear energy system as a whole, including the question of CO2 emissions, CO2 reduction costs, radioactive releases from the power plant, the fuel chain and the final disposal. The material balance of different fuel cycles with thermal and fast reactors isdiscussed.
Lecture notesLecture slides will be distributed as handouts and in digital form
151-0156-00LSafety of Nuclear Power Plants Information W4 credits2V + 1UH.‑M. Prasser, V. Dang, L. Podofillini
AbstractKnowledge 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.
ObjectiveDeep 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) Loss-of-coolant 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 notesScript:
Hand-outs of lecture slides will be distributed
Audio recording of lectures will be provided
Script "Short introduction into basics of nuclear power"
LiteratureS. Glasston & A. Sesonke: Nuclear Reactor Engineering, Reactor System Engineering, Ed. 4, Vol. 2., Chapman & Hall, NY, 1994
Prerequisites / NoticePrerequisites:
Recommended in advance (not binding): 151-0163-00L Nuclear Energy Conversion
151-0166-00LPhysics of Nuclear Reactor IIW4 credits3GK. Mikityuk, A. Pautz, S. Pelloni
AbstractReactor 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 state-of-the-art calculational methodologies in the above context.
ObjectiveStudents are introduced to advanced methods of reactor physics analysis for nuclear power plants.
ContentCross-sections 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 Monte-Carlo. Modeling of fuel depletion. Lattice calculations and cross-section 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 notesHand-outs will be provided on the website.
LiteratureChapters from various text books on Reactor Theory, etc.
151-1906-00LMultiphase FlowsW4 credits3GF. Coletti
AbstractIntroduction 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.
ObjectiveThe main learning objectives are:
- identify multiphase flow regimes and relevant non-dimensional parameters
- distinguish spatio-temporal scales at play for each phase
- quantify mutual coupling between different phases
- apply fundamental principles in complex real-world flows
- combine insight from theory, experiments, and numerics
ContentSingle particle and multi-particle dynamics in laminar and turbulent flows; basics of suspension rheology; effects of surface tension on the formation, evolution and motion of bubbles and droplets; free-surface flows and wind-wave interaction; imaging techniques and modeling approaches.
Lecture notesLecture slides are made available.
LiteratureSuggested readings are provided for each topic.
Prerequisites / NoticeFundamental knowledge of fluid dynamics is essential.
151-0530-00LNonlinear Dynamics and Chaos IIW4 credits4GG. Haller
AbstractThe internal structure of chaos; Hamiltonian dynamical systems; Normally hyperbolic invariant manifolds; Geometric singular perturbation theory; Finite-time dynamical systems
ObjectiveThe course introduces the student to advanced, comtemporary concepts of nonlinear dynamical systems analysis.
ContentI. The internal structure of chaos: symbolic dynamics, Bernoulli shift map, sub-shifts of finite type; chaos is numerical iterations.

II.Hamiltonian dynamical systems: conservation and recurrence, stability of fixed points, integrable systems, invariant tori, Liouville-Arnold-Jost 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. Finite-time dynamical system; detecting Invariant manifolds and coherent structures in finite-time flows
Lecture notesStudents have to prepare their own lecture notes
LiteratureBooks will be recommended in class
Prerequisites / NoticeNonlinear Dynamics I (151-0532-00) or equivalent
151-0116-10LHigh Performance Computing for Science and Engineering (HPCSE) for Engineers II Information W4 credits4GP. Koumoutsakos, S. M. Martin
AbstractThis course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Uncertainty Quantification and Propagation including the implementation of relevant algorithms on HPC architectures.
ObjectiveThe course will teach
- programming models and tools for multi and many-core architectures
- fundamental concepts of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences
ContentHigh Performance Computing:
- Advanced topics in shared-memory programming
- Advanced topics in MPI
- GPU architectures and CUDA programming

Uncertainty Quantification:
- Uncertainty quantification under parametric and non-parametric modeling uncertainty
- Bayesian inference with model class assessment
- Markov Chain Monte Carlo simulation
Lecture notes
Class notes, handouts
Literature- Class notes
- Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein
- CUDA by example, J. Sanders and E. Kandrot
- Data Analysis: A Bayesian Tutorial, D. Sivia and J. Skilling
- An introduction to Bayesian Analysis - Theory and Methods, J. Gosh, N. Delampady and S. Tapas
- Bayesian Data Analysis, A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari and D. Rubin
- Machine Learning: A Bayesian and Optimization Perspective, S. Theodorides
Prerequisites / NoticeStudents must be familiar with the content of High Performance Computing for Science and Engineering I (151-0107-20L)
227-0161-00LMolecular and Materials Modelling Information W4 credits2V + 2UD. Passerone, C. Pignedoli
AbstractThe 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 hands-on for realistic systems.
The modern methods of "big data" analysis applied to the screening of chemical structures will be introduced with examples.
ObjectiveThe 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, X-ray, 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 notesA script will be made available and complemented by literature references.
LiteratureD. 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.
529-0442-00LAdvanced Kinetics Information W6 credits3GJ. Richardson
AbstractThis 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.
ObjectiveThe theory of quantum dynamics is derived from the time-dependent 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 quantum-mechanical picture as well as within the classical approximation. The approximations leading to conventional transition-state 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 notesWill be available online.
LiteratureD. J. Tannor, Introduction to Quantum Mechanics: A Time-Dependent Perspective
R. D. Levine, Molecular Reaction Dynamics
S. Mukamel, Principles of Nonlinear Optical Spectroscopy
Prerequisites / Notice529-0422-00L Physical Chemistry II: Chemical Reaction Dynamics
529-0434-00LPhysical Chemistry V: Spectroscopy Information W4 credits3GH. J. Wörner
Abstractthermal 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
ObjectiveWhen 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 (Born-Oppenheimer approximation) as well as rotations and vibrations of the nuclear motion (normal-mode 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 symmetry-adapted molecular orbitals, and to use the symmetry of states to derive selection rules of molecules,
* to use a quantum-mechanical picture to explain intensities of vibrational progressions of an electronic spectrum (Franck-Condon factors), and
* to determine selection rules for spectroscopic transitions based on a qualitative evaluation of the dipole matrix element.
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 quantum-mechanically, in particular
- selection rules and symmetry/group theory
: separation of electrons and nuclei (Born-Oppenheimer approximation)
- separation of vibrations and rotations (normal mode approximation)
and how to use these tools to understand and predict spectra qualitatively
Lecture notesis available on the lecture website
529-0440-00LPhysical Electrochemistry and ElectrocatalysisW6 credits3GT. Schmidt
AbstractFundamentals 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.
ObjectiveProviding an overview and in-depth 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.
ContentReview of electrochemical thermodynamics, description electrochemical kinetics, Butler-Volmer equation, Tafel kinetics, simple electrochemical reactions, electron transfer, Marcus Theory, fundamentals of electrocatalysis, elementary reaction processes, rate-determining 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 notesWill be handed out during the Semester
LiteraturePhysical Electrochemistry, E. Gileadi, Wiley VCH
Electrochemical Methods, A. Bard/L. Faulkner, Wiley-VCH
Modern Electrochemistry 2A - Fundamentals of Electrodics, J. Bockris, A. Reddy, M. Gamboa-Aldeco, Kluwer Academic/Plenum Publishers
227-0948-00LMagnetic Resonance Imaging in MedicineW4 credits3GS. Kozerke, M. Weiger Senften
AbstractIntroduction to magnetic resonance imaging and spectroscopy, encoding and contrast mechanisms and their application in medicine.
ObjectiveUnderstand the basic principles of signal generation, image encoding and decoding, contrast manipulation and the application thereof to assess anatomical and functional information in-vivo.
ContentIntroduction to magnetic resonance imaging including basic phenomena of nuclear magnetic resonance; 2- and 3-dimensional 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 notesD. Meier, P. Boesiger, S. Kozerke
Magnetic Resonance Imaging and Spectroscopy
227-0303-00LAdvanced PhotonicsW6 credits2V + 2U + 1AA. Emboras, M. Burla, A. Dorodnyy
AbstractThe lecture gives a comprehensive insight into various types of nano-scale photonic devices, physical fundamentals of their operation, and an overview of the micro/nano-fabrication technologies. Following applications of nano-scale photonic structures are discussed in details: detectors, photovoltaic cells, atomic/ionic opto-electronic devices and integrated microwave photonics.
ObjectiveGeneral training in advanced photonic devices with an in-depth understanding of the fundamentals of theory, fabrication, and characterization. Hands-on 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.
ContentThe 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/nano-fabrication technologies for advanced optoelectronic devices: introduction and device examples.
• Comprehensive insight into the physical mechanisms that govern ionic-atomic devices, present the techniques required to fabricate ultra-scaled nanostructures and show some applications in digital and neuromorphic computing.
• Introduction to microwave photonics (MWP), microwave photonic links, photonic techniques for microwave signal generation and processing.
Lecture notesThe 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, Wiley-VCH
• Electrochemical Methods: Fundamentals and Applications, A. Bard and L. Faulkner, John Willey & Sons, Inc.

• 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 / NoticeBasic knowledge of semiconductor physics, physics of the electromagnetic filed and thermodynamics.
227-0390-00LElements of MicroscopyW4 credits3GM. Stampanoni, G. Csúcs, A. Sologubenko
AbstractThe lecture reviews the basics of microscopy by discussing wave propagation, diffraction phenomena and aberrations. It gives the basics of light microscopy, introducing fluorescence, wide-field, confocal and multiphoton imaging. It further covers 3D electron microscopy and 3D X-ray tomographic micro and nanoimaging.
ObjectiveSolid introduction to the basics of microscopy, either with visible light, electrons or X-rays.
ContentIt 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 X-ray microscopy.
During selected exercises in the lab, several sophisticated instrument will be explained and their capabilities demonstrated.
LiteratureAvailable Online.
227-0396-00LEXCITE Interdisciplinary Summer School on Bio-Medical Imaging Restricted registration - show details
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:
W4 credits6GS. Kozerke, G. Csúcs, J. Klohs-Füchtemeier, S. F. Noerrelykke, M. P. Wolf
AbstractTwo-week summer school organized by EXCITE (Center for EXperimental & Clinical Imaging TEchnologies Zurich) on biological and medical imaging. The course covers X-ray imaging, magnetic resonance imaging, nuclear imaging, ultrasound imaging, optoacoustic imaging, infrared and optical microscopy, electron microscopy, image processing and analysis.
ObjectiveStudents 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.
ContentTwo-week summer school on biological and medical imaging. The course covers concepts and implementations of X-ray imaging, magnetic resonance imaging, nuclear imaging, ultrasound imaging, optoacoustic imaging, infrared and optical microscopy and electron microscopy. Multi-modal and multi-scale 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 notesPresentation slides, Web links
Prerequisites / NoticeThe 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:
227-0434-10LMathematics of Information Information W8 credits3V + 2U + 2AH. Bölcskei
AbstractThe class focuses on mathematical aspects of

1. Information science: Sampling theorems, frame theory, compressed sensing, sparsity, super-resolution, spectrum-blind sampling, subspace algorithms, dimensionality reduction

2. Learning theory: Approximation theory, greedy algorithms, uniform laws of large numbers, Rademacher complexity, Vapnik-Chervonenkis dimension
ObjectiveThe aim of the class is to familiarize the students with the most commonly used mathematical theories in data science, high-dimensional data analysis, and learning theory. The class consists of the lecture, exercise sessions with homework problems, and of a research project, which can be carried out either individually or in groups. The research project consists of either 1. software development for the solution of a practical signal processing or machine learning problem or 2. the analysis of a research paper or 3. a theoretical research problem of suitable complexity. Students are welcome to propose their own project at the beginning of the semester. The outcomes of all projects have to be presented to the entire class at the end of the semester.
ContentMathematics 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, super-resolution, spectrum-blind sampling, subspace algorithms (ESPRIT), estimation in the high-dimensional noisy case, Lasso

3. Dimensionality reduction: Random projections, the Johnson-Lindenstrauss Lemma

Mathematics of Learning

4. Approximation theory: Nonlinear approximation theory, best M-term approximation, greedy algorithms, fundamental limits on compressibility of signal classes, Kolmogorov-Tikhomirov epsilon-entropy of signal classes, optimal compression of signal classes

5. Uniform laws of large numbers: Rademacher complexity, Vapnik-Chervonenkis dimension, classes with polynomial discrimination
Lecture notesDetailed lecture notes will be provided at the beginning of the semester.
Prerequisites / NoticeThis 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 "401-4944-20L Mathematics of Data Science" by Prof. A. Bandeira. The two courses are designed to be complementary.

H. Bölcskei and A. Bandeira
227-0159-00LSemiconductor Devices: Quantum Transport at the Nanoscale Information W6 credits2V + 2UM. Luisier, A. Emboras
AbstractThis 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 Non-equilibrium 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.
ObjectiveThe 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 drift-diffusion processes fails. The purpose of this class is to explore and understand the displacement of electrons through nanoscale device structures based on state-of-the-art quantum transport methods and to get familiar with the underlying equations by developing his own nanoelectronic device simulator.
ContentThe 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 Non-equilibrium Green's Function formalisms as solution to the Schrödinger equation
- Self-consistent Schödinger-Poisson simulations
- Quantum transport simulations of resonant tunneling diodes and quantum well nano-transistors
- Top-of-the-barrier simulation approach to nano-transistor
- Electron interactions with their environment (phonon, roughness, impurity,...)
- Multi-band transport models
Lecture notesLecture slides are distributed every week and can be found at
LiteratureRecommended textbook: "Electronic Transport in Mesoscopic Systems", Supriyo Datta, Cambridge Studies in Semiconductor Physics and Microelectronic Engineering, 1997
Prerequisites / NoticeBasic knowledge of semiconductor device physics and quantum mechanics
227-0395-00LNeural SystemsW6 credits2V + 1U + 1AR. Hahnloser, M. F. Yanik, B. Grewe
AbstractThis 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.
ObjectiveThis 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.
ContentFrom 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 / NoticeBefore taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L).

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.
363-0588-00LComplex Networks Information W4 credits2V + 1UF. Schweitzer
AbstractThe 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
ContentNetworks 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 self-organized 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 notesThe lecture slides are provided as handouts - including notes and literature sources - to registered students only.
All material is to be found on Moodle at the following URL:
LiteratureSee handouts. Specific literature is provided for download - for registered students, only.
Prerequisites / NoticeThere are no pre-requisites for this course. Self-study 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.
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