statistics phd epfl

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(last modified 30.4.2024)

Contact person: Professor Anthony Davison

Bachelor (3rd and 4th semester)

• Probabilités (MATH-230)
• Statistique (MATH-240)

Bachelor (5th and 6th semesters)

• Linear Models (MATH-341)
• Time Series (MATH-342)
• Randomisation and Causation (MATH-336)
• Risk and Environmental Sustainability (MATH-XXX)
• Stochastic Processes (MATH-332)
• Mesure et Intégration (MATH-303)
• Statistical Inference (MATH-562)
• Regression Methods (MATH-408)
• Multivariate Statistics (MATH-444)
• Applied Statistics (MATH-516)
• Statistical Computation and Visualisation (MATH-517)
• Statistical Machine Learning (MATH-412)
• Statistical Theory (MATH-442)
• Nonparametric Estimation and Inference (MATH-YYY)
• Empirical Processes (MATH-ZZZ)
• Biostatistics (MATH-449)
• Applied Biostatistics (MATH-493)
• Statistics for Genomic Data Analysis (MATH-474)
• Statistical Genetics (MATH-438)
• Statistical Analysis of Network Data (MATH-448)
• Stochastic Simulation (MATH-414)
• Probability Theory (MATH-432)
• Inference on Graphs (MATH-455)

Description

All students take the second-year courses in Probability (MATH-230) and Statistics (MATH-230). MATH-230 provides a careful introduction to the key notions of probability, including limit theorems crucial for statistical applications. MATH-240 gives a rigorous introduction to the elements of statistical inference (estimation, testing, confidence intervals) for a scalar parameter based on a random sample. 

Linear Models (MATH-341) and Time Series (MATH-342) are core third-year courses for the Statistics track. They consider two important modes of departure from the standard "i.i.d" setup encountered in the second year. In MATH-341, the data remain independent but have differing parameters, subject to linear constraints, and with Gaussian behaviour. In MATH-342, the data may have the same distribution (i.e., are stationary), but are typically dependent. Like many of the other courses below, these two courses describe methods for analysis of existing data, but do not say how to plan investigations that lead to secure inferences. Randomisation and Causation (MATH-336), has two main topics, namely how randomisation can be used to design experiments to give strong data from which reliable inferences can be drawn, and the circumstances under which causal inferences (e.g., `behaviour A causes health outcome B') can be drawn from observational data. Stochastic Processes (MATH-332), which is also strongly recommended, considers more general dependence structures than in MATH-341, emphasising both dependence and non-stationarity, primarily through the Markov property.  It is a basic course for further studies in random processes, and also a source of models for statistical work.  Risk and Environmental Sustainability (MATH-XXX), a new course to be given for the first time in the spring semester 2025, will discuss basic stochastic models for rare events and forecast assessment, with applications to environmental problems.  Students considering the possibility of higher studies in statistics are strongly encouraged to take Mesure and Intégration (MATH-303), which provides theoretical underpinning necessary for the study of advanced mathematical statistics.

There is an EPFL  MSc in Statistics . The master level courses in statistics cover more advanced material, building on the third year courses. Statistical Inference (MATH-562) gives an overview of the key ideas on which statistical inferences are based, including the likelihood and Bayesian frameworks. Regression Methods (MATH-408) is the natural follow-up to Linear Models (MATH-341), exploring models for non-Gaussian response variables, more complex dependence structures in which some variables may be treated as random, and situations where smoothing is important. Multivariate Statistics (MATH-444) treats inference for collections of random vectors, which are widespread in applications. Statistical Machine Learning (MATH-412) studies methods of supervised and unsupervised machine learning from a mathematical viewpoint.  Statistical Computation and Visualisation (MATH-517) and Applied Statistics (MATH-516) together form the applied statistics sequence at master's level.   Further theory, following on from MATH-562, further statistical theory is developed in Statistical Theory (MATH-442).   In addition to the above master courses on general theory and methods, various courses on more specialised topics are available; not all of these are given every year. Biostatistics (MATH-449) presents some of the core methods and applications of statistics in the life sciences and medicine. Applied Biostatistics (MATH-493) focuses on the use of the software package R for the analysis of biomedical data. Statistics for Genomic Data Analysis (MATH-443) explores the key challenges and statistical techniques used in the analysis of massive genomic data. Statistical Genetics (MATH-438) covers key probability models and statistical methods that are used for the analsyis of genetic data. Statistical Analysis of Network Data (MATH-448) describes methods and models for the analysis of data that arise in connection with networks, which have become very prominent in recent years.   Other theory courses are Nonparametric Estimation and Inference (MATH-YYY) and Empirical Processes (MATH-ZZZ). Stochastic Simulation (MATH-414) is an introduction to Monte Carlo methods, which are widely used in statistical applications, especially for Bayesian inference. Probability Theory (MATH-432) takes a second look at probability using the tools of measure theory and is strongly recommended for students wishing to pursue graduate study in statistics. Inference for Graphics (MATH-455) concerns learning from network data, and is a natural complement to MATH-448. Some other mathematics courses related to statistics All courses in the Probability track are particularly recommended. Numerical Analysis (MATH-250), Advanced Numerical Analysis (MATH-351) and Numerical Integration of Stochastic Differential Equations (MATH-452) contain useful background for nonparametric statistics, statistical optimisation and functional data analysis respectively. 

Computational Linear Algebra (MATH-453) considers numerical methods to solve large-scale linear algebra problems, which can be particularly pertinent in multivariate and high-dimensional statistics when massive amounts of data must be stored and manipulated for the purposes of inference.

Discrete Optimization (MATH-261) has important links with statistical inference problems related to discrete structures.  Nonlinear Optimization (MATH-329) and Convexity (MATH-461) discuss aspects of high dimensional geometry that are central to many methods of modern high dimensional statistics.

Other courses and related minors

Mathematics students can take a few credits outside mathematics, some of which may be related to statistics.  Examples are Convex Optimization (MGT-418), Mathematics of Data (EE-556) and Optimization for Machine Learning (CS-439).   Machine Learning (CS-433) is also a good choice, but it has several overlaps with Statistical Machine Learning (MATH-412); interested students should therefore take MATH-412 and, if needed, CS-433 outside their curriculum.

There is a minor in  Data Science .

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An extensive survey reveals post EPFL careers

What's next for EPFL graduates after their studies? A major survey conducted in 2022 shows how much EPFL graduates contribute to research, innovation and the economy in Switzerland and abroad. EPFL graduates are highly integrated into the job market, occupy key positions, earn salaries above the Swiss average and follow a wide variety of career paths. 92% are satisfied with their professional situation. The number of female EPFL architects and engineers is rising sharply, but there are still clear inequalities between men and women.

Who are the alumni and alumnae?

EPFL currently has over 43,000 alumni and alumnae. The career survey was conducted among 29,630 members of this population, who graduated between 1980 and 2019 and for whom EPFL Alumni had a contact email. This is a young and growing population, with over 50% having graduated less than 10 years ago and under 41 years of age. Coming from 15 different sections - the most numerous being Architecture, Physics, Microengineering and Computer Science - the majority left EPFL following their Master's degree (63%), while 27% obtained a PhD. 21% of the school's graduates are women, a figure that is also rising steadily: in recent years, women have accounted for 27% each year, compared with just 8% in the 1980s.

A total of 3214 people agreed to take part in the survey, which was carried out jointly by EPFL Alumni and the EPFL Teaching Support Center. The sample is broadly representative of the reference population in all respects.

A population strongly integrated into the job market, active in a variety of sectors and positions

95% of survey respondents said they were professionally active, including 8.3% in self-employment. Only 1% of respondents said they were looking for a job, a figure which demonstrates the strong attractiveness of the School's talent on the job market.

The vast majority of respondents (72%) work in the private sector, while 23% hold a position in the public sector and 5% in a not-for-profit organization. The wide variety of sectors in which our respondents work clearly demonstrates the wealth of career paths available to them after EPFL. The 5 most frequently cited sectors are IT and Telecommunications (13.8%), Higher education (8.2%), Architecture (7.9%), Finance (7.6%) and Construction (7%).

Positions held are equally varied. More than half of respondents work in technology or research positions, around 20% in management and strategy, and around 5% in supply chain. In detail, the most frequent answers are IT (12.1%), Engineering (9.8%), Research and development (9.8%), Project management (7.6%) and Architecture (6.9%). A PhD remains a prerequisite for positions in research or academia, as these are the two positions most often cited by respondents with this type of degree.

Finally, we note that 92% are satisfied with their professional situation, including 51% who are strongly satisfied. Respondents were particularly happy with the content of their jobs (94% satisfied, including 59% strongly) and their responsibilities and autonomy (94%, including 62% strongly).

Click for larger view

EPFL graduates have above-average responsibilities and salaries

Over 53% of survey respondents claim to have managerial responsibility - 24% in a "top management" position (senior executive, board member...) and 29.2% in a "middle management" position.

The median salary among respondents is 120-140 k CHF, while the Swiss average is 80 k CHF and that of higher education is 120 k CHF. These salaries remain higher in the private sector (average 120-140 k CHF) than in the public sector (100-120 k CHF). Management, finance, luxury goods and legal affairs appear to be the best-paid sectors and functions.

EPFL studies are valued and careers in line with them

The careers of EPFL talent are closely related to their studies: 86% of respondents say that their job is directly linked to their studies - 42% of them strongly. These figures are particularly strong for people from Computer Science (98%), Architecture (94%) and Life Sciences (93%). Conversely, the link may be less consistent for people from Physics (69%), Mechanical Engineering (79%) or Mathematics (81%) sections.

21% of respondents said they had set up a business at some point in their career, a particularly high figure among those from the Architecture (46%) and Management, Technology and Entrepreneurship (43%) sections, which is again in direct line with their studies.

Feedback from survey respondents also shows strong recognition of EPFL diplomas on the job market, both in Switzerland and abroad. In fact, 97% of respondents stated that their diploma was recognized in Switzerland, 96% in the rest of Europe, 92% in North America and 90% in the rest of the world.

A highly international community, 70% active in Switzerland

The reference population includes 136 different nationalities, 60 of which are represented among the survey respondents. This international dimension is also reflected in the fact that 80% of respondents speak at least two languages (95% French, 89% English, 50% German).

Despite this wide diversity of origins, EPFL talent largely remains in Switzerland, where over 70% of respondents say they work - Vaud (41.9%), Geneva (14.9%) and Zurich (10.8%) being cited most frequently. Switzerland's attractiveness can be seen in the fact that 53% of non-Swiss European citizens stay on to work after their studies, as do 51% of citizens from the rest of the world (excluding North America).

Other countries where respondents most often work are France (23%), the USA (15%), Germany (12%), England and Canada (6% each).

Major career inequalities between men and women

The disparity between the careers of men and women with an EPFL degree remains high. 68% of women surveyed said they worked full-time, well above the 41.5% average for women living in Switzerland. However, this is still well below the 88% of male EPFL graduates who responded to the survey. Similarly, the arrival of a child has a far greater impact on women's careers than on those of men. After the birth of a first child, only 48% of women surveyed work full-time, while 84% of men continue to do so.

These inequalities have a direct impact on the career progression of EPFL women. Among female respondents, only 11% claim to occupy a "top management" position, compared with 28% of men. Similarly, the median salary for women is only CHF 80-100 k, compared with CHF 120-140 k for men.

Continuing education as a career tool

Technical, analytical and problem-solving skills are the skills acquired at EPFL most often cited by survey respondents. But continuing education after EPFL is just as essential, as 90% of respondents pointed out, recommending project management, communication and team management as additional skills to acquire after an EPFL course. Digital and sustainability are also cited as major areas of interest for future continuing education.

To find out more

Read the executive summary (24 pages).

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EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,500 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of more than 17,000 people, including over 12,500 students and 4,000 researchers from more than 120 different countries.

PhD position at the Catchment Hydrology and Geomorphology Lab

The Catchment Hydrology and Geomorphology Lab ( CHANGE ) led by Prof. Sara Bonetti at EPFL in Sion is looking for a PhD student to work on the SNSF-funded project SOCscape (Assessing soil carbon dynamics in landscapes of complex topography).

Mountain regions are hotspots of land cover and climatic changes where the interactions of climate, parent material, topography, and biota define key ecosystem services tied to the cycling and storage of carbon in soil. Yet, our ability to quantify and predict soil carbon stocks and lateral fluxes across such highly heterogeneous landscapes is still limited. By integrating field monitoring, soil biogeochemical analysis, and numerical modeling of coupled ecohydrological and geomorphological processes, SOCscape will evaluate the dynamics of lateral soil carbon redistribution across environmentally complex and geochemically distinct landscapes.

As a PhD student, you will have the opportunity to further current understanding in this field through a combination of field monitoring, data analysis, and numerical modeling. The project is a joint collaboration between EPFL, ETH Zürich (ETHZ), University of Lausanne (UNIL), and the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), with a team encompassing (eco)hydrologists, soil scientists, and geomorphologists.

• Perform original research in the area of ecohydrology and soil carbon cycling.
• Lead and contribute to publications for high-impact journals.
• Contribute to broad Lab activities (including co-supervision of MSc students and teaching assistance).
• Attend and present at international conferences.
• A MSc in Environmental Sciences or Engineering.
• Strong interest in ecohydrological research.
• Experience with ecohydrological and/or geomorphological field monitoring is valued.
• Hands-on experience with numerical modeling and analysis of large datasets.
• Excellent written and oral communication skills in English.
• Ability to work independently as well as in a team.
• Creative, enthusiastic, and willing to learn.
• Self-motivated and have a sense for scientific novelty.
• Experience with scientific writing is valued.
• A stimulating and international working environment in the new EPFL-Valais campus in Sion.
• Competitive salary and excellent educational conditions.
• Opportunity to perform state-of-the-art research in one of the most dynamic scientific institutions in Europe.
• Opportunity to closely collaborate with PhD students, postdocs, and a technician at CHANGE, as well as with our collaborators at ETHZ, WSL, and UNIL.

Interested applicants should supply the following documents:

• A brief (max 1 page) letter of motivation/interest in the project
• Grades from master and bachelor studies
• Contact details of 2-3 references

Screening will start on Nov. 1st but the position will remain open until filled. To be eligible for a PhD at EPFL, note that candidates also need to apply to the Doctoral Program in Civil and Environmental Engineering (EDCE) . If you have any questions, please feel free to contact Prof. Sara Bonetti ( [email protected] ). Applications sent to this address will not be considered.

Contract Start Date : 03/01/2025 

Activity Rate : 100.00 

Contract Type: PhD Student

Duration: 1-year fixed-term contract, renewable for 4 years according to EPFL rules. 

Reference: 1147 

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Coursebooks

Statistical physics for optimization & learning

PHYS-642 / 4 credits

Teacher(s): Krzakala Florent Gérard , Loureiro Bruno , Saglietti Luca , Zdeborová Lenka

Language: English

Remark: Next time: Spring 2027

Every 2 years

This course covers the statistical physics approach to computer science problems, with an emphasis on heuristic & rigorous mathematical technics, ranging from graph theory and constraint satisfaction to inference to machine learning, neural networks and statitics.

Website of the lecture:  https://idephics.github.io/EPFLDoctoralLecture2023/

Mainly a theoretical course, with exercises in the analytical methods and usage of the related algorithms in high-dimensional problems in statistics, optimization and machine learning

Evaluation of the lecture based on homeworks given during the whole semester

Statistical physics, replica method, cavity method, neural networks, theory of machine learning, combinatorial optimization, community detection, graphical models, message passins algorithms.

Learning Prerequisites

Required courses.

For physicists : PHYS 512 & a good knowlegde of statistical physics. For mathematicians: Probability & Introductory statistical physics will be helpful FOR CS/STI: Basic probability & Information theory/Entropy/Coding will be helpful

Learning Outcomes

By the end of the course, the student must be able to:

• To study a range of problems in computer science and learning, and derive formulas and algorithms for their solution, using technics from statistical physics.
• To study a range of problems in computer science and learning
• derive formulas and algorithms for their solution, using technics from statistical physics

Moodle Link

• https://go.epfl.ch/PHYS-642

In the programs

• Exam form: During the semester (session free)
• Subject examined: Statistical physics for optimization & learning
• Lecture: 28 Hour(s)
• Exercises: 28 Hour(s)
• Type: optional

Reference week

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Postdoctoral and PhD Positions in River Microbiome Research, EPFL, Switzerland

SumPositions in River Microbiome Research: The River Ecosystems Laboratory at the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland is offering a unique opportunity for a postdoctoral research associate and a PhD student to contribute to the global glacier-fed stream microbiome research.

Postdoctoral and PhD Positions in River Microbiome Research

Designation:

• Postdoctoral Research Associate – Two-Year Position (100%)
• PhD Student – Four-Year Position (100%)

Research Area:

Exploring the microbial diversity and ecosystem dynamics of glacier-fed streams worldwide, utilizing cutting-edge metagenomics, ecological analyses, and GIS-based glacier assessments.

EPFL, Switzerland

Eligibility/Qualification:

Postdoctoral position:.

• PhD in microbial ecology, ecological, evolutionary, or environmental sciences
• Strong expertise in metagenomics, ecophylogenetics, and bioinformatics
• Excellent communication skills and work ethic

PhD Position:

• MSc or equivalent in ecological, evolutionary, or environmental sciences
• Basic knowledge of microbial ecology and bioinformatics
• Motivation, strong work ethic, and effective communication abilities

Desired Qualifications (for both positions):

• Proficiency in quantitative skills
• Curiosity and interest in interdisciplinary collaboration
• Excellent team integration capabilities

Job Description:

• Conduct research on glacier-fed stream microbiomes using available data
• Analyze 16S and 18 rRNA sequencing, metagenomics, and environmental parameters
• Contribute to research publications and scholarly activities

How to Apply:

Interested candidates should submit a single PDF including a cover letter, CV, three key publications, and contact information for four references to [email protected] . Pre-application inquiries can be directed to [email protected] .

Last Date for Apply:

Applications will be reviewed starting by the end of November and will continue until positions are filled. The application deadline is open until positions are filled.

This is a fantastic opportunity to engage in groundbreaking research at a world-class institution. For more information, visit River Ecosystems Laboratory and discover the fascinating world of glacier-fed stream microbiomes. Apply now and be part of this innovative scientific journey!

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