- Professor: Anders Meibom
- Teacher: Paolo Benettin
Course description – Content (could change)
- Introduction to ecological economics: The economy as a sub-system of the global environment: entropy, carrying capacity, environmental services, ecological crises, natural capital, sustainability (PT, March 10)
- Introduction to market economics: willingness to pay, preferences, marginalism, demand, supply, markets, prices, elasticities, non-market goods, externalities (PT, March 17)
- Introduction to cost-benefit analysis (CBA), Assessment of environmental impacts and valuation of natural resources (PT, March 24)
- Long-term discounting incl. hyperbolic discounting and limited substitutability of natural capital, planetary boundaries vs. CBA (FV, March 31)
- Uncertainty and irreversibility, prospect theory, economics of innovation (FV, April 7)
- Green growth or degrowth? Sufficiency and quality of life (PT, April 14)
- Environmental policy instruments: voluntary approaches, regulation, economic instruments, example of the US Clean Air Act (MV, April 28)
- How to think of Ecological Economics, Foundations, and Implications (SN, May 5)
- Environmental policy-making: acceptance of environmental policies, co-operation and retaliation, course wrap-up (MV or PT, May 12)
- Final exam (May 19)
- Professor: Philippe Thalmann
- Professor: Marc Vielle
- Professor: Frank Vöhringer
- Teacher: Gino Karim Claude Baudry
- Teacher: Sascha Nick
The analysis of large-scale time series is a difficult task for which most classical statistical methods (such as the maximum likelihood) are often inadequate. Indeed, these standard methods typically entail an unrealistic computational burden and are often unable to estimate complex (state-space) models. The estimation of the models used as an approximate to the stochastic behavior of various sensors (navigation sensors, oscillators, etc.) or natural phenomena (biological, chemical, etc.) is an important example of such large-scale signal processing problem. To alleviate the shortcomings of maximum likelihood methods (and other classical methods) used in this context, several new estimators have been proposed in the statistics and signal processing literature. In this course, we will discuss a new statistical method called the Generalized Method of Wavelet Moments (GMWM). This method is often the only feasible estimation approach that can be applied for complex models which are used in engineering and natural sciences. The students get familiar with its open-source distribution and practical usage that is then leveraged in the project on their own data or other use cases.
Schedule:
- Professor: Jan Skaloud
- Teacher: Mohammad Abedininajafabadi
Communication proficiency is one of the most important results of a good Ph.D. and postdoc experience and it is valued
equally in academia and in industry. EPFL Ph.D. students and postdocs are expected to have excellent written, oral, and
graphic skills in order to transmit their ideas effectively.
The course is divided into modules that are related to typical communication tasks that PhDs and postdocs are expected to perform. There is also a module related to publication ethics.
By the end of the course, the student must be able to:
• Demonstrate improved oral, written, and graphical communication skills for engineering research
- Teacher: Mathias Haindl Carvallo
- Teacher: Ian F. C. Smith
- Professor: Eugen Brühwiler
- Professor: Davide Leonetti
- Professor: Alain Nussbaumer
Learning Fourier Series and Boundary Value Problems with a view to a
variety of science and engineering problems. Learn the use of special
functions like Bessel functions and applications. Introduce the doctoral
students to general Sturm-Liouville problems and applications.
- Professor: Andrea Rinaldo
- Teacher: Cristiano Trevisin
- Professor: Alexis Berne
- Professor: Valentin Simeonov
- Professor: Philippe Thalmann
- Professor: Marc Vielle
- Professor: Frank Vöhringer
