Search result: Catalogue data in Spring Semester 2023

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 (Link) after having received the credits.)
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. Csontos, 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.
- 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 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.
• Molecular Electronics: An Introduction to Theory and Experiment, Elke Scheer and Juan Carlos Cuevas

• 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.
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesProblem-solvingassessed
Social CompetenciesCommunicationassessed
Cooperation and Teamworkassessed
Personal CompetenciesIntegrity and Work Ethicsassessed
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: Link.
W4 credits6GS. Kozerke, B. Menze, M. P. Wolf, U. Ziegler Lang
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: Link
227-0432-00LLearning, Classification and Compression Information W4 credits2V + 1UE. Riegler
AbstractThe 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.
ObjectiveAfter attending this lecture and participating in the exercise sessions, students will have acquired a working knowledge of learning theory, classification, and compression.
Content1. Learning Theory
(a) Framework of Learning
(b) Hypothesis Spaces and Target Functions
(c) Reproducing Kernel Hilbert Spaces
(d) Bias-Variance 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 notesDetailed lecture notes will be provided.
Prerequisites / NoticeThis course is aimed at students with a solid background in measure theory and linear algebra and basic knowledge in functional analysis.
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 and exercise sessions with homework problems.
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
Subject-specific CompetenciesConcepts and Theoriesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Personal CompetenciesCreative Thinkingassessed
Critical Thinkingassessed
Self-awareness and Self-reflection assessed
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 + 1UG. Casiraghi
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.
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.
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
363-0543-00LAgent-Based Modelling of Social SystemsW3 credits2V + 1UG. Vaccario
AbstractAgent-based modeling is introduced as a bottom-up 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.
ObjectiveA successful participant of this course is able to
- understand the rationale of agent-based 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 agent-based models using Python and visualize the output
ContentThis full-featured course on agent-based 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.

Agent-based modeling is introduced as a bottom-up 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 agent-based modeling, weekly exercise classes provide practical skills. Using the Python programming language, the participants implement agent-based models in guided and in self-chosen projects, which they present and jointly discuss.
Lecture notesThe lecture slides will be available on the Moodle platform, for registered students only.
LiteratureSee handouts. Specific literature is provided for download, for registered students only.
Prerequisites / NoticeParticipants 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.

Self-study tasks are provided as home work for small teams (2-4 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 paper-based dictionary is permitted.
The group project to be handed in at the beginning of July will count 30% to the final grade.
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".
701-1708-00LInfectious Disease Dynamics Restricted registration - show details W4 credits2VR. R. Regös, S. Bonhoeffer, R. D. Kouyos, T. Stadler
AbstractThis 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.
ObjectiveAttendees 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 host-pathogen system ("about").
ContentAfter 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 notesSlides and script of the lecture will be available online.
LiteratureThe 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 / NoticeBasic knowledge of population dynamics and population genetics as well as linear algebra and analysis will be an advantage.
Subject-specific CompetenciesConcepts and Theoriesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Social CompetenciesCommunicationassessed
Personal CompetenciesCreative Thinkingfostered
Critical Thinkingassessed
701-1236-00LMeasurement Methods in Meteorology and Climate Research Information W1 credit1VM. Hirschi, D. Michel
AbstractThe 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.
ObjectiveAims 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.
ContentProblems 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 eddy-correlation technique. Discussion of technical realizations of complex observing systems (radiosondes, automatic weather stations, radar, wind profilers). Demonstration of instruments.
Lecture notesStudents can download a copy of the lectures as PDF-files.
Literature- Emeis, Stefan: Measurement Methods in Atmospheric Sciences, In situ and remote. Bornträger 2010, ISBN 978-3-443-01066-9
- Brock, F. V. and S. J. Richardson: Meteorological Measurement Systems, Oxford University Press 2001, ISBN 0-19-513451-6
- Thomas P. DeFelice: An Introduction to Meteorological Instrumentation and Measurement. Prentice-Hall 2000, 229 p., ISBN 0-13-243270-6
- 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 / NoticeThe lecture focuses on physical atmospheric parameters while lecture 701-0234-00 concentrates on the chemical quantities. The lectures are complementary, together they provide the instrumental basics for the lab course 701-0460-00. Contact hours of the lab course are such that the lectures can be attended (which is recommended).
701-0234-00LAtmospheric Chemistry: Instruments and Measuring Techniques Information W1 credit1VU. Krieger
AbstractMeasuring Techniques: Environmental Monitoring, Trace Gas Detection, Remote Sensing, Aerosol Characterization, Techniques used in the laboratory.
ObjectiveFind 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.
ContentEs 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.
LiteratureB. J. Finnlayson-Pitts, J. N. Pitts, "Chemistry of the Upper and Lower Atmosphere", Academic Press, San Diego, 2000
Prerequisites / NoticeMethodenvorlesung zu den Praktika 701-0460-00 und 701-1230-00. Die Kontaktzeiten in diesen Praktika sind so abgestimmt, dass der (empfohlene) Besuch der Vorlesung möglich ist.

Voraussetzungen: Atmosphärenphysik I und II
701-0412-00LClimate SystemsW3 credits2GL. Gudmundsson, D. Schumacher
AbstractThis 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.
ObjectiveStudents 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 notesCopies of the slides are provided in electronic form.
LiteratureA 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 / NoticeTeaching: Lukas Gudmundsson & Dominik Schumacher, several keynotes to special topics by other professors
Course taught in german/english, slides in english
701-1252-00LClimate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation Restricted registration - show details W3 credits2V + 1UD. N. Bresch, R. Knutti
AbstractThe course introduces the concepts of predictability, probability, uncertainty and probabilistic risk modelling and their application to climate modeling and the economics of climate adaptation.
ObjectiveStudents 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.
ContentThe 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 pre-emptive 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 pre-emptive risk management to optimally allocate resources
Lecture notesPowerpoint slides will be made available.
LiteratureMany papers for in-depth study will be referred to during the lecture. For the exercises the CLIMADA platform- Link - will be (extensively) used.
Prerequisites / NoticeHands-on 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)
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Media and Digital Technologiesfostered
Project Managementfostered
Social CompetenciesCommunicationassessed
Cooperation and Teamworkfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence fostered
Sensitivity to Diversityfostered
Personal CompetenciesAdaptability and Flexibilityassessed
Creative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsfostered
Self-direction and Self-management fostered
851-0655-00LETH Global Development Summer SchoolW3 credits6GA. Rom, K. W. Axhausen, P. Krütli, M. Makridis, M. Mertens
AbstractThe ETH Global Development Summer School provides young researchers with the opportunity to work on current and sustainability-related topics in interdisciplinary and intercultural teams. Focus is given not only to teaching theoretical knowledge but also to solving specific case studies.
ObjectiveWithin 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 prototyping-skills to increase hands-on 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
ContentThe 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 real-world challenge related to mobility. Students will work in interdisciplinary teams. The summer school will be held in person in Kumasi, Ghana.
Literaturefurther information and registration:
Prerequisites / NoticeNo 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, technical-related 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 Covid-19 situation, the course might have to change format or be postponed.
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesfostered
Method-specific CompetenciesAnalytical Competenciesassessed
Media and Digital Technologiesfostered
Project Managementassessed
Social CompetenciesCommunicationassessed
Cooperation and Teamworkassessed
Customer Orientationfostered
Leadership and Responsibilityassessed
Self-presentation and Social Influence assessed
Sensitivity to Diversityassessed
Personal CompetenciesAdaptability and Flexibilityassessed
Creative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsassessed
Self-awareness and Self-reflection assessed
Self-direction and Self-management assessed
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