Other doctoral courses (EDOC)
External courses (.)
Master courses (.)
You have to choose one of these courses during your first Ph.D year. Be aware that you have to have 4 credits to pass the first year.
Spatial statistics and analysis ENG-440 (5 credits) – FALL
The main objective is to make the students understand the importance of the spatial issues in environmental sciences and engineering, for example for mapping and interpolation. Presentation of different concepts and techniques devoted to spatial data.
Image processing for earth observation ENV-540 (3 credits) – FALL
This course covers optical remote sensing from satellites and airborne platforms. The different systems are presented. The students will acquire skills in image processing and machine learning to extract end-products, such as land cover or risk maps, from the images.
Advanced composites in Engineering Structures CIVIL-443 (3 credits) – FALL
The objective of the course is to: 1. Introduce topics in properties, processing, mechanical behavior, characterization, analysis and structural design of Fiber Reinforced Composites 2. Help students develop their research skills through independent investigations on research topics.
Design and analysis of experiments in materials science and engineering MSE-629 (2 credits)
Provide the student with the skills and tools necessary for a wise and efficient orgqnization of his-her experimental work in all fields of materials science and technology (development, processing and caracterization of materials)
Air pollution and climate change ENV-400 (5 credits) – SPRING
A survey course describing the origins of air pollution and climate change.
Fourier analysis and boundary value problems ENV-614 (4 credits) – SPRING
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.
Structural stability CIVIL-369 (4 credits) SPRING
Advanced topics in structural stability; elastic & inelastic column buckling; beam-columns; lateral-torsional buckling of bridge girders; nonlinear geometric effects; frame stability; computational formulation of stability theory; stiffness & flexibility methods
Advanced continuum mechanics CIVIL-422 (3 credits) FALL
Reading class of classic text book of Lawrence Malvern “Introduction to the Mechanics of a Continuous Medium”. A special emphasis will be put on advanced topics, including finite kinematics, and non-linear material behavior. Applications will cover both solids and structures fluid mechanics.
Applied biostatistics MATH-493 (5 credits) – SPRING
This course covers topics in applied biostatistics, with an emphasis on practical aspects of data analysis using R statistical software. Topics include types of studies and their design and analysis, high dimensional data analysis (genetic/genomic) and other topics as time and interest permit.
Biostatistics MATH-449: (5 credits) – SPRING
Statistics for life sciences – seems to cover a lot of basic statistic information that was present in the cancelled courses so it may still be relevant.
Statistiques multivariables avec R ENV-521 (4 credits) – FALL
Introduction to multivariate data analysis and modelling. The course helps for a critical choice of methods and their integration in a research planning. It prepares for complexe data analysis in various fields of environemental sciences. Use of dedicated R libraries.
Understanding statistics and Experimental design BIO-449 (4 credits) – FALL
This course is neither an introduction to the mathematics of statistics nor an introduction to a statistics program such as R. The aim of the course is to understand statistics from its experimental design and to avoid common pitfalls of statistical reasoning. There is space to discuss ongoing work.
Scientific programming for engineers MATH-611 (4 credits) FALL
The students will acquire a solid knowledge on the processes necessary to design, write and use scientific software, including the analysis of results. Modeling aspects, which constrain software design, will lead the students to algorithmic and complexity concepts inherent to all numerical calculation.
Applied data analysis CS-401 (6 credits) – FALL
This course teaches the basic techniques and practical skills required to make sense out of a variety of data, with the help of the most acclaimed software tools in the data science world: pandas, scikit-learn, Spark, etc.
Distributed information systems CS-423 (4 credits) – SPRING
Information retrieval, data mining and knowledge bases
Systems for data science CS-449 (6 credits) – SPRING
Principles for understanding and building systems for managing and analysing large amounts of data – requires the Bachelor unit Introduction to database systems cs-322
Information Science in Engineering CIVIL-711 (4 credits) – SPRING (variable)
An introduction to engineering-relevant computer-science concepts that are hardware and software independent. Outcomes include knowledge of the limits of computing, improved ability to understand the true value of new developments and capabilities to effectively select good computing methodologies
Data Analysis for Science and Engineering MATH-710 (4 credits) – POSTPONED
Machine learning CS-433 (7 credits) – FALL
Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.
Applied machine learning MICRO-455 (4 credits) – FALL
Real-world engineering applications must cope with a large dataset of dynamic variables, which cannot be well approximated by classical or deterministic models. This course gives an overview of methods from Machine Learning for the analysis of non-linear, highly noisy and multi dimensional data.
Machine learning for engineers EE-613 (EDOC) (4 credits) – FALL/ variable
The objective of this course is to give an overview of machine learning techniques used for real-world applications, and to teach how to implement and use them in practice.
Image analysis and pattern recognition EE-451 (4 credits) – SPRING
This course gives an introduction to the main methods of image analysis and pattern recognition.
Optimization and simulation MATH-600 (4 credits) – SPRING
Master state-of-the art methods in discrete optimization and simulation. Work involves: – reading the material beforehand – class hours to discuss the material and solve problems – homework
Mathematical modelling of behaviour MATH-463 (5 credits) – FALL
Discrete choice models allow for the analysis and prediction of individuals’ choice behavior. The objective of the course is to introduce both methodological and applied aspects, in the field of marketing, transportation, and finance.
The course introduces ‘classical’ statistical design of experiments, particularly designs for blocking, full and fractional factorial designs with confounding, and response surface methods. Topics covered include (restricted) randomization and blocking, sample size and power calculations, confounding, and basics of analysis-of-variance methods for analysis including random effects and nesting. 3h per week (5 credits?).
Principles of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
Statistics and experimental design – 3 credits
The course aims to introduce basic concepts of statistics and experimental design. The course will cover topics from the description of data set to multilinear regression analysis.
The open source software R (http://www.r-project.org) has revolutionized the statistical data analysis for most bioscience disciplines. R environment covers an unmatched spectrum of statistical tools including an efficient programming language for automating time-consuming analysis routines. Due to its popularity, R is continuously updated and extended with the latest analysis tools that are available in the different research fields. The R environment is completely free and runs on all common operating systems. This course provides a short introduction into the R environment and then tackles in more depth the problem of model selection (the task of selecting a statistical model from a set of candidate models).
- See the EDCE regulations about credit allocation.
- No credits can be earned from Bachelor courses
- Credits from master courses and courses given at other universities must be pre-approved by the thesis director and the director of the doctoral program. The credits will be awarded only after the reception of an official transcript describing explicitly that an exam was passed, and providing the exact number of ECTS credits.
- Credits can be obtained from short courses or summer schools under the following conditions:
- The thesis director and the director of the doctoral program must approve the course
- An official transcript must be provided indicating the number of course hours, plus the number of ECTS credits if available.
- A formal exam must be passed to evaluate the learning outcomes of the course (a certificate of participation is not sufficient)
Note that the number of ECTS credits proposed by the course organizer does not necessarily correspond to the number of ECTS credits awarded by the doctoral program. The general rule is that a 5-day course is worth 1 ECTS.
- No credit can be obtained from conferences, seminars, symposium, workshops, internships, etc.