Search result: Catalogue data in Spring Semester 2021

Doctoral Department of Mechanical and Process Engineering Information
Doctoral and Post-Doctoral Courses
151-0111-00LResearch Seminar in Fluid Dynamics
Internal research seminar for graduate students and scientific staffs of the IFD
Z0 credits2SF. Coletti, P. Jenny, T. Rösgen, O. Supponen
AbstractCurrent research projects at the Institute of Fluid Dynamics are presented and discussed.
ObjectiveExchange on current internal research projects. Training of presentation skills.
ContentCurrent research projects in Fluid Dynamics
151-9904-00LApplied Compositional Thinking for Engineers Information W4 credits3GE. Frazzoli, A. Censi, J. Lorand
AbstractThis course is an introduction to applied category theory specifically targeted at persons with an engineering background. We focus on the benefits of applied category theory for thinking explicitly about abstraction and compositionality. The course will favor a computational/constructive approach, with concrete exercises in the Python.
ObjectiveIn many domains of engineering it would be beneficial to think explicitly about abstraction and compositionality, to improve both the understanding of the problem and the design of the solution. However, the problem is that the type of math which could be useful to engineers is not traditionally taught.

Applied category theory could help a lot, but it is quite unreachable by the average engineer. Recently many good options appeared for learning applied category theory; but none satisfy the two properties of 1) being approachable; and 2) highlighting how applied category theory can be used to formalize and solve concrete problems of interest to engineers.

This course will fill this gap. This course's goal is not to teach category theory for the sake of it. Rather, we want to teach the "compositionality way of thinking" to engineers; category theory will be just the means towards it. This implies that the presentation of materials sometimes diverges from the usual way to teach category theory; and some common concepts might be de-emphasized in favor of more obscure concepts that are more useful to an engineer.

The course will favor a computational/constructive approach: each concept is accompanied by concrete exercises in the programming language Python.
Throughout the course, we will discuss many examples related to autonomous robotics, because it is at the intersection of many branches of engineering: we can talk about hardware (sensing, actuation, communication) and software (perception, planning, learning, control) and their composition.
Content## Intended learning outcomes

# Algebraic structures

The student is able to recognize algebraic structure for a familiar engineering domain. In particular we will recall
the following structures: monoid, groups, posets, monoidal posets, graphs.

The student is able to translate such algebraic structure in a concrete implementation using the Python language for the purpose of solving a computational problem.

# Categories and morphisms

The student is able to recognize categorical structure for a familiar engineering domain, understand the notion of object, morphism, homsets, and the properties of associativity and unitality.

The student is able to quickly spot non-categories (formalizations in which one of the axioms fails, possibly in a subtle way) and is informed that there exist possible generalizations (not studied in the course).

The student is able to translate a categorical structure into a concrete implementation using the Python language.

The student is able to recognize the categorical structure in the basic algebraic structures previously considered.

The student is able to use string diagrams to represent morphisms; and to write a Python program to draw such a representation.

# Products, coproducts, universality

# Recognizing and using additional structure

The student is able to spot the presence of the following structures: Monoidal structure, Feedback structure (Trace),
Locally posetal/lattice structure , Dagger/involutive structure.

# Functorial structure.

The student is able to recognize functorial structures in a familiar engineering domain.

The student can understand when there is a functorial structure between instances of a problem and solutions of the problem, and use such structure to write programs that use these compositionality structures to achieve either more elegance or efficiency (or both).

# The ladder of abstractions

The student is able to think about scenarios in which one can climb the ladder of abstractions. For example, the morphisms in a category can be considered objects in another category.

# Compact closed structure.

# Co-design

The student knows co-design theory (boolean profunctors + extensions) and how to use it to formalize design problems in their area of expertise.

The student knows how to use the basics of the MCPD language and use it to solve co-design problems.

# Rosetta stone

The student understands explicitly the connection between logic and category theory and can translate concepts back and forth.

The student understands explicitly the constructive nature of the presentation of category theory given so far.

The student is able to understand what is an "equational theory" and how to use it concretely.

The student understands the notion of substructural logics; the notion of polycategories; and linear logic. Mention of *-autonomous categories.

The student can translate the above in an implementation.

# Monadic structure

The student is able to recognize a monadic structure in the problem.

# Operads and operad-like structures.
Lecture notesSlides and notes will be provided.
LiteratureB. Fong, D.I. Spivak, Seven Sketches in Compositionality: An Invitation to Applied Category Theory (

A. Censi, D. I. Spivak, J. Tan, G. Zardini, Mathematical Foundations of Engineering Co-Design (Own manuscript, to be published)
Prerequisites / NoticeAlgebra: at the level of a bachelor’s degree in engineering.

Analysis: ODEs, dynamical systems.

Familiarity with basic physics, electrical engineering, mechanical engineering, mechatronics concepts (at the level of bachelor's degree in engineering).

Basics of Python programming.
» Course Catalogue of ETH Zurich
151-0906-00LFrontiers in Energy Research Information
This course is only for doctoral students.
W2 credits2SC. Schaffner
AbstractDoctoral students at ETH Zurich working in the broad area of energy present their research to their colleagues, their advisors and the scientific community. Each week a different student gives a 50-60 min presentation of their research (a full introduction, background & findings) followed by discussion with the audience.
ObjectiveThe key objectives of the course are:
(1) participants will gain knowledge of advanced research in the area of energy;
(2) participants will actively participate in discussion after each presentation;
(3) participants gain experience of different presentation styles;
(4) to create a network amongst the energy research doctoral student community.
ContentDoctoral students at ETH Zurich working in the broad area of energy present their research to their colleagues, to their advisors and to the scientific community. There will be one presentation a week during the semester, each structured as follows: 20 min introduction to the research topic, 30 min presentation of the results, 30 min discussion with the audience.
Lecture notesSlides will be available on the Energy Science Center pages(
151-0520-00LMultiscale ModelingW4 credits3GD. Kochmann
AbstractTheoretical foundations and numerical applications of multiscale modeling in solid mechanics, from atomistics all the way up to the macroscopic continuum scale with a focus on scale-bridging methods (including the theory of homogenization, computational homogenization techniques, modeling by methods of atomistics, coarse-grained atomistics, mesoscale models, multiscale constitutive modeling).
ObjectiveTo acquire the theoretical background and practical experience required to develop and use theoretical-computational tools that bridge across scales in the multiscale modeling of solids.
ContentMicrostructure and unit cells, theory of homogenization, computational homogenization by the finite element method and Fourier-based techniques, discrete-to-continuum coupling methods, atomistics and molecular dynamics, coarse-grained atomistics for crystalline solids, quasicontinuum techniques, analytical upscaling methods and models, multiscale constitutive modeling, selected topics of multiscale modeling.
Lecture notesLecture notes and relevant reading materials will be provided.
LiteratureNo textbook is required. Reference reading materials are suggested.
Prerequisites / NoticeContinuum Mechanics I or II and Computational Mechanics I or II (or equivalent).
151-0528-00LTheory of Phase TransitionsW4 credits3GL. Guin, D. Kochmann
AbstractThis course addresses two major examples of phase transitions, namely solid-solid phase transformations and solidification. We focus on the modeling of the propagation of phase boundaries (surface of strain discontinuity or solidification front) in continuum media. Both the sharp-interface model and related numerical modeling techniques based on the phase-field method are introduced.
ObjectiveThe students are able to:
- Use mechanical and/or thermodynamic balance laws to formulate a continuum model for problems involving phase transformations in 1D, 2D, and 3D.
- Distinguish between the different modeling techniques used for the propagation of phase boundaries and discuss their underlying assumptions.
- Apply the concepts of thermodynamics to continuous media in order to derive thermodynamically consistent models.
- Model the evolution of a solidification front using the phase-field method.
Content1. Mechanics of bars
2. The Ericksen’s bar problem: solid-solid phase transformation in 1D
3. Review of classical thermodynamics
4. Continuum theory for phase boundaries in 3D
5. Solidification: a free-boundary problem with interfacial structure
6. Phase-field model for solidification
7. Selected topics involving phase transitions
Lecture notesLecture notes will be provided for reference. Students are strongly encouraged to take their own notes during class.
LiteratureNo textbook required; relevant reference material will be suggested.
Prerequisites / NoticeContinuum Mechanics I. Having taken or taking Continuum Mechanics II in parallel would be helpful.
151-0540-00LExperimental MechanicsW4 credits2V + 1UJ. Dual, T. Brack
Abstract1. General aspects like transfer functions, vibrations, modal analysis, statistics, digital signal processing, phase locked loop, 2. Optical methods 3. Piezoelectricity 4. Electromagnetic excitation and detection 5. Capacitive Detection
ObjectiveUnderstanding, quantitative modelling and practical application of experimental methods for producing and measuring mechanical quantities (motion, deformation, stresses,..)
Content1. General Aspects: Measurement chain, transfer functions, vibrations and waves in continuous systems, modal analysis, statistics, digital signal analysis, phase locked loop. 2. Optical methods ( acousto optic modulation, interferometry, holography, photoelasticity, shadow optics, Moire methods ) 3. Piezoelectric materials: basic equations, applications, accelerometer ) 4. Electomagnetic excitation and detection, 5. Capacitive detection
Practical training and homeworks
Lecture notesno
Prerequisites / NoticePrerequisites: Mechanics I to III, Physics, Elektrotechnik
151-0566-00LRecursive Estimation Information W4 credits2V + 1UR. D'Andrea
AbstractEstimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
ObjectiveLearn the basic recursive estimation methods and their underlying principles.
ContentIntroduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle.
Lecture notesLecture notes available on course website:
Prerequisites / NoticeRequirements: Introductory probability theory and matrix-vector algebra.
151-0593-00LEmbedded Control SystemsW4 credits6GJ. S. Freudenberg, M. Schmid Daners
AbstractThis course provides a comprehensive overview of embedded control systems. The concepts introduced are implemented and verified on a microprocessor-controlled haptic device.
ObjectiveFamiliarize students with main architectural principles and concepts of embedded control systems.
ContentAn embedded system is a microprocessor used as a component in another piece of technology, such as cell phones or automobiles. In this intensive two-week block course the students are presented the principles of embedded digital control systems using a haptic device as an example for a mechatronic system. A haptic interface allows for a human to interact with a computer through the sense of touch.

Subjects covered in lectures and practical lab exercises include:
- The application of C-programming on a microprocessor
- Digital I/O and serial communication
- Quadrature decoding for wheel position sensing
- Queued analog-to-digital conversion to interface with the analog world
- Pulse width modulation
- Timer interrupts to create sampling time intervals
- System dynamics and virtual worlds with haptic feedback
- Introduction to rapid prototyping
Lecture notesLecture notes, lab instructions, supplemental material
Prerequisites / NoticePrerequisite courses are Control Systems I and Informatics I.

This course is restricted to 33 students due to limited lab infrastructure. Interested students please contact Marianne Schmid Daners (E-Mail:
After your reservation has been confirmed please register online at

Detailed information can be found on the course website
151-0840-00LOptimization and Machine Learning
Note: previous course title until FS20 "Principles of FEM-Based Optimization and Robustness Analysis".
W4 credits2V + 2UB. Berisha, D. Mohr
AbstractThe course teaches the basics of nonlinear optimization and concepts of machine learning. An introduction to the finite element method allows an extension of the application area to real engineering problems such as structural optimization and modeling of material behavior on different length scales.
ObjectiveStudents will learn mathematical optimization methods including gradient based and gradient free methods as well as established algorithms in the context of machine learning to solve real engineering problems, which are generally non-linear in nature. Strategies to ensure efficient training of machine learning models based on large data sets define another teaching goal of the course.

Optimization tools (MATLAB, LS-Opt, Python) and the finite element program ABAQUS are presented to solve both general and real engineering problems.
Content- Introduction into Nonlinear Optimization
- Design of Experiments DoE
- Introduction into Nonlinear Finite Element Analysis
- Optimization based on Meta Modeling Techniques
- Shape and Topology Optimization
- Robustness and Sensitivity Analysis
- Fundamentals of Machine Learning
- Generalized methods for regression and classification, Neural Networks, Support Vector machines
- Supervised and unsupervised learning
Lecture notesLecture slides and literature
151-0944-00LCase Studies on Earth's Natural Resources
Does not take place this semester.
W3 credits3SM. Mazzotti
AbstractBy working on case studies, built around everyday consumer products, and by applying engineering principles (e.g. material and energy balances), students will gain insight into natural resources, their usage in today's society, the challenges and the opportunities ensuing from the need to make their use long-term sustainable.
ObjectiveThe students are supposed to gain insight about our natural resources, and how their usage and supply relate to our society and to us as individuals. The students will analyse how the natural resources form and change, how they are extracted and used, and how we can utilize them in a sustainable way.
ContentThe students will analyze processes and products in terms of their use of natural resources. The study will use everyday consumer products as examples, will use engineering principles together with physics and chemistry fro the analysis, and will be based on documentation collected by the students withe the help of lecturer and assistants. Through these examples, the students will be made familiar with issues about the circular economy and recycling.
Lecture notesHandouts during the class.
LiteratureWalther, John V., "Earth's natural resources", (2014) Jones & Bartlett Learning // Oberle, B., Bringezu, S., Hatfield-Dodds, S., Hellweg, S., Schandl, H., Clement, J., "Global Resources Outlook 2019: Natural resources for the future we want - A Report of the International Resource Panel", (2019) United Nations Environment Programme.
Prerequisites / NoticeStudents must be enrolled in a MSc or doctoral program at ETH Zurich.
151-1053-00LThermo- and Fluid DynamicsZ0 credits2KP. Jenny, R. S. Abhari, G. Haller, C. Müller, N. Noiray, D. Poulikakos, T. Rösgen, A. Steinfeld
AbstractCurrent advanced research activities in the areas of thermo- and fluid dynamics are presented and discussed, mostly by external speakers.

The talks are public and open also for interested students.
ObjectiveKnowledge of advanced research in the areas of thermo- and fluid dynamics
ContentCurrent advanced research activities in the areas of thermo- and fluid dynamics are presented and discussed, mostly by external speakers.
101-0178-01LUncertainty Quantification in Engineering Information W3 credits2GS. Marelli, B. Sudret
AbstractUncertainty quantification aims at studying the impact of aleatory and epistemic uncertainty onto computational models used in science and engineering. The course introduces the basic concepts of uncertainty quantification: probabilistic modelling of data (copula theory), uncertainty propagation techniques (Monte Carlo simulation, polynomial chaos expansions), and sensitivity analysis.
ObjectiveAfter this course students will be able to properly pose an uncertainty quantification problem, select the appropriate computational methods and interpret the results in meaningful statements for field scientists, engineers and decision makers. The course is suitable for any master/Ph.D. student in engineering or natural sciences, physics, mathematics, computer science with a basic knowledge in probability theory.
ContentThe course introduces uncertainty quantification through a set of practical case studies that come from civil, mechanical, nuclear and electrical engineering, from which a general framework is introduced. The course in then divided into three blocks: probabilistic modelling (introduction to copula theory), uncertainty propagation (Monte Carlo simulation and polynomial chaos expansions) and sensitivity analysis (correlation measures, Sobol' indices). Each block contains lectures and tutorials using Matlab and the in-house software UQLab (
Lecture notesDetailed slides are provided for each lecture. A printed script gathering all the lecture slides may be bought at the beginning of the semester.
Prerequisites / NoticeA basic background in probability theory and statistics (bachelor level) is required. A summary of useful notions will be handed out at the beginning of the course.

A good knowledge of Matlab is required to participate in the tutorials and for the mini-project.
101-0190-08LUncertainty Quantification and Data Analysis in Applied Sciences
Does not take place this semester.
The course should be open to doctoral students from within ETH and UZH who work in the field of Computational Science. External graduate students and other auditors will be allowed by permission of the instructors.
W3 credits4GE. Chatzi, P. Koumoutsakos
AbstractThe course presents fundamental concepts and advanced methodologies for handling and interpreting data in relation with models. It elaborates on methods and tools for identifying, quantifying and propagating uncertainty through models of systems with applications in various fields of Engineering and Applied science.
ObjectiveThe course is offered as part of the Computational Science Zurich (CSZ) ( graduate program, a joint initiative between ETH Zürich and University of Zürich. This CSZ Block Course aims at providing a graduate level introduction into probabilistic modeling and identification of engineering systems.
Along with fundamentals of probabilistic and dynamic system analysis, advanced methods and tools will be introduced for surrogate and reduced order models, sensitivity and failure analysis, parallel processing, uncertainty quantification and propagation, system identification, nonlinear and non-stationary system analysis.
ContentThe topics to be covered are in three broad categories, with a detailed outline available online (see Learning Materials).
Track 1: Uncertainty Quantification and Rare Event Estimation in Engineering, offered by the Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich (18 hours)
Lecturers: Prof. Dr. Bruno Sudret, Dr. Stefano Marelli
Track 2: Bayesian Inference and Uncertainty Propagation, offered the by the System Dynamics Laboratory, University of Thessaly, and the Chair of Computational Science, ETH Zurich (18 hours)
Lecturers: Prof. Dr. Costas Papadimitriou, Dr. Georgios Arampatzis, Prof. Dr. Petros Koumoutsakos
Track 3: Data-driven Identification and Simulation of Dynamic Systems, offered the by the Chair of Structural Mechanics, ETH Zurich (18 hours)
Lecturers: Prof. Dr. Eleni Chatzi, Dr. Vasilis Dertimanis
The lectures will be complemented via a comprehensive series of interactive Tutorials will take place.
Lecture notesThe course script is composed by the lecture slides, which will be continuously updated throughout the duration of the course on the CSZ website.
LiteratureSuggested Reading:
Track 2 : E.T. Jaynes: Probability Theory: The logic of Science
Track 3: T. Söderström and P. Stoica: System Identification, Prentice Hall International, Link see Learning Materials.
Xiu, D. (2010) Numerical methods for stochastic computations - A spectral method approach, Princeton University press.
Smith, R. (2014) Uncertainty Quantification: Theory, Implementation and Applications SIAM Computational Science and Engineering,
Lemaire, M. (2009) Structural reliability, Wiley.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008) Global Sensitivity Analysis - The Primer, Wiley.
Prerequisites / NoticeIntroductory course on probability theory
Fair command on Matlab
227-0224-00LStochastic Systems
Does not take place this semester.
W4 credits2V + 1Uto be announced
AbstractProbability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering.
ObjectiveStochastic dynamic systems. Optimal control and filtering of stochastic systems. Examples in technology and finance.
Content- Stochastic processes
- Stochastic calculus (Ito)
- Stochastic differential equations
- Discrete time stochastic difference equations
- Stochastic processes AR, MA, ARMA, ARMAX, GARCH
- Kalman filter
- Stochastic optimal control
- Applications in finance and engineering
Lecture notesH. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts
327-2140-00LFocused Ion Beam and Applications Restricted registration - show details
Number of participants limited to 6. PhD students will be asked for a fee.

Registration form: (Link)
W1 credit2PP. Zeng, A. G. Bittermann, S. Gerstl, L. Grafulha Morales, J. Reuteler
AbstractThe introductory course on Focused Ion Beam (FIB) provides theoretical and hands-on learning for new operators, utilizing lectures, demonstrations and hands-on sessions.
Objective- Set-up, align and operate a FIB-SEM successfully and safely.
- Accomplish operation tasks and optimize microscope performances.
- Perform sample preparation (TEM lamella, APT probe…) using FIB-SEM.
- Perform other FIB techniques, such as characterization
- At the end of the course, students will know how to set-up FIB-SEM, how to prepare TEM lamella/APT probe and how to utilize FIB techniques.
ContentThis course provides FIB techniques to students with previous SEM experience.
- Overview of FIB theory, instrumentation, operation and applications.
- Introduction and discussion on FIB and instrumentation.
- Lectures on FIB theory.
- Lectures on FIB applications.
- Practicals on FIB-SEM set-up, cross-beam alignment.
- Practicals on site-specific cross-section and TEM lamellar preparation.
- Lecture and demonstration on FIB automation.
Literature- Detailed course manual.
- Giannuzzi, Stevie: Introduction to focused ion beams instrumentation, theory, techniques, and practice, Springer, 2005.
- Orloff, Utlaut, Swanson: High resolution focused ion beams: FIB and its applications, Kluwer Academic/Plenum Publishers, 2003.
Prerequisites / NoticeThe students should fulfil one or more of these prerequisites:
- Prior attendance to the ScopeM Microscopy Training SEM I: Introduction to SEM (327-2125-00L).
- Prior SEM experience.
327-2224-00LMaP Distinguished Lecture Series on Additive Manufacturing
Does not take place this semester.
This course is primarily designed for MSc and doctoral students. Guests are welcome.
W1 credit2Sfurther lecturers
AbstractThis course is an interdisciplinary colloquium on Additive Manufacturing (AM) involving different internationally renowned speakers from academia and industry giving lectures about their cutting-edge research, which highlights the state-of-the-art and frontiers in the AM field.
ObjectiveParticipants become acquainted with the state-of-the-art and frontiers in Additive Manufacturing, which is a topic of global and future relevance from the field of materials and process engineering. The self-study of relevant literature and active participation in discussions following presentations by internationally renowned speaker stimulate critical thinking and allow participants to deliberately discuss challenges and opportunities with leading academics and industrial experts and to exchange ideas within an interdisciplinary community.
ContentThis course is a colloquium involving a selected mix of internationally renowned speaker from academia and industry who present their cutting-edge research in the field of Additive Manufacturing. The self-study of relevant pre-read literature provided in advance to each lecture serves as a basis for active participation in the critical discussions following each presentation.
Lecture notesSelected scientific pre-read literature (max. three articles per lecture) relevant for and discussed at the end of each individual lecture is posted in advance on the course web page
Prerequisites / NoticeParticipants should have a solid background in materials science and/or engineering.
327-2225-00LMaP Distinguished Lecture Series on Soft Robotics
This course is primarily designed for MSc and doctoral students. Guests are welcome.
W1 credit2SR. Katzschmann, L. Schefer
AbstractThis course is an interdisciplinary colloquium on Soft Robotics involving different internationally renowned speakers from academia and industry giving lectures about their cutting-edge research, which highlights the state-of-the-art and frontiers in the Soft Robotics field.
ObjectiveParticipants become acquainted with the state-of-the-art and frontiers in Soft Robotics, which is a topic of global and future relevance from the field of materials and process engineering. The self-study of relevant literature and active participation in discussions following presentations by internationally renowned speakers stimulate critical thinking and allow participants to deliberately discuss challenges and opportunities with leading academics and industrial experts and to exchange ideas within an interdisciplinary community.
ContentThis course is a colloquium involving a selected mix of internationally renowned speaker from academia and industry who present their cutting-edge research in the field of Soft Robotics. The self-study of relevant pre-read literature provided in advance to each lecture serves as a basis for active participation in the critical discussions following each presentation.
Lecture notesSelected scientific pre-read literature (max. three articles per lecture) relevant for and discussed during the lectures is posted in advance on the course web page.
Prerequisites / NoticeParticipants should have a solid background in materials science and/or engineering.
363-0764-00LProject ManagementW2 credits2VC. G. C. Marxt
AbstractThe course gives a detailed introduction into various aspects of classic and agile project management. Established concepts and methods for initiating, planning and executing projects are introduced and major challenges discussed. Additionally the course covers different agile and hybrid project management concepts.
ObjectiveProjects are not only the base of work in modern enterprises but also the primary type of cooperation with customers. Students of ETH will often work in or manage projects in the course of their career. Good project management knowledge is not only a guarantee for individual but also for company wide success.

The goal of this course is to give a detailed introduction into project management, more specific participants
- will understand the basics of successful classic and agile project management
- are able to apply the concepts and methods of project management in their day to day work
- are able to identify different project management practices and are able to suggest improvements
- will contribute to projects in your organization in a positive way
- will be able to plan and execute projects successfully.
ContentThe competitiveness of companies is driven by the development of a concise strategy and its successful implementation. Especially strategy execution poses several challenges to senior management: clear communication of goals, ongoing follow up of activities, a sound monitoring and control system. All these aspect are covered by successfully implementing and applying program and project management. As an introductory course we will focus mainly on project management.
In the last decade project management has become an important discipline in management and several internationally recognized project management methods can be found: PMBOK, IPMA ICB, PRINCE 2, etc. These frameworks have proven to be very useful in day-to-day work.
Unfortunately the environment companies are working in has changed parallel to the rise of PM as a discipline. Incremental but even more important fundamental changes happen more often and much faster than a decade ago. Experience has shown that the classic PM approaches lack the inherent dynamics to cope with these challenges. So overtime new methods have surfaced, such as SCRUM. These methods are called Agile Project Management methods and follow a dynamic model of reality, called complex adaptive systems perspective.
This course will cover both classic and agile project management topics. The first part of the semester will lay the basics by discussing the classic way of planning, organizing and executing a project based on its life cycle. Topics covered include: drafting project proposals, stake holder analysis, different aspects of project planning, project organization, project risk management, project execution, project control, leadership in projects incl. conflict mitigation strategies, termination and documentation. In the second part basic conceptual topics for agile project management such as the agile manifesto, SCRUM, Lean, Kanban, XP, rapid results are covered. The course tries to tap into pre-existing knowledge of the participants using a very interactive approach including in-class discussion, short exercises and case studies.
Lecture notesNo
The lecture slides and other additional material (papers, book chapters, case studies, etc.) will be available for download from Moodle before each class.
363-1039-00LIntroduction to NegotiationW3 credits2GM. Ambühl
AbstractThe course introduces students to the concepts, theories, and strategies of negotiation and is enriched with an extensive exploration of real-life case-study examples.
ObjectiveThe objective of the course is to teach students to recognize, understand, and approach different negotiation situations, by relying on a range of primarily quantitative and some qualitative analytical tools.
ContentWe all negotiate on a daily basis – on a personal level with friends, family, and service providers, on a professional level with employers and clients, among others. Additionally, negotiations are constantly unfolding across various issues at the political level, from solving armed conflicts to negotiating trade and market access deals.

The course aims to provide students with a toolbox of analytical methods that can be used to identify and disentangle negotiation situations, as well as serve as a reference point to guide action in practice. The applicability of these analytical methods is illustrated through examples of negotiation situations from international politics and business.

The theoretical part of the course covers diverse perspectives on negotiation: with a key focus on game theory, but also covering Harvard principles of negotiation, as well as the negotiation engineering approach developed by Prof. Ambühl at ETH Zurich. The course also dedicates some time to focus on conflict management as a specific category of negotiation situations and briefly introduces students to the social aspects of negotiation, based on the insights from psychology and behavioral economics.

The empirical part of the course draws on case-studies from the realm of international politics and business, including examples from Prof. Ambühl’s work as a career diplomat. Every year, the course also hosts two guest lecturers – representatives from politics or business leaders, who share practical experience on negotiations from their careers.
LiteratureThe list of relevant references will be distributed in the beginning of the course.
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