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  

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 

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