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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Statistical Physics For Optimization and Learning

A set of lectures given at epfl in 2021 by lenka zdeborova and florent krzakala.

Main lecturers: Pr. Florent Krzakala, head of IdePHICS lab and Pr. Lenka Zdeborova, head of SPOC lab

Teaching assitants: Bruno Loureiro and Luca Saglietti

Main topics

Interest in the methods and concepts of statistical physics is rapidly growing in fields as diverse as theoretical computer science, probability, machine learning, discrete mathematics, optimization and compressed sensing. This course will cover this rich and active interdisciplinary research landscape.

More specifically, we will review the statistical physics approach to problems ranging from graph theory (percolation, community detection) to discrete optimization and constraint satisfaction (satisfiability, coloring, bisection) and to inference and learning problems (learning in neural networks, clustering of data and of networks, compressed sensing or sparse linear regression, low-rank matrix and tensor factorization, etc.).

We will expose theoretical methods of analysis (replica, cavity, …) algorithms (message passing, spectral methods, …), discuss concrete applications, highlight rigorous justifications as well as present the connection to the physics of glassy and disordered systems.

The course is designed to be accessible to graduate students and researchers of all natural science and engineering disciplines with a basic knowledge of probability and analysis. Advanced training in any of the above fields is not requisite.

References: Information, Physics and Computation (Oxford Graduate Texts) , 2009, M. Mézard, A. Montanari

Statistical Physics of inference: Thresholds and algorithms , Advances in Physics 65, 5 2016, L. Zdeborová & F. Krzakala

Times and Place

The lecture will take place on zoom: click here Recorded lectures will be avalible on SWITCHtube click here

Main lectures will on Friday, from 10:00-12:00 and exercices will be on Thursday, 10:00-12:00

Discussion will be on a slack channel (ask for being invited)

Detailed plan:

Lecture notes: Chapter 1-13

Lecture 1, 26/2/2021 “Curie-Weiss model”

Homework 1, due on Wed 10.3 (no delay allowed): Exercice 1.4 (Metropolis-Hastings algorithm) & Exercise 1.5:(Glauber Algorithm). Please upload your homework on Moodle .

Lecture 2, 05/3/2021 “Your first replica computations”

Homework 2, due on Wed 17.3 (no delay allowed): Exercise 2.3 (Mean-field algorithm and state evolution) & Exercise 3.1:(Wishart Matrix). Please upload your homework on Moodle .

Lecture 3, 12/3/2021 “Belief Propagation”

Homework 3, due on Wed 24.3 (no delay allowed): Exercise 4.1 (Representing problems by graphical models) & Exercise 4.2:(Bethe free entropy). Please upload your homework on Moodle .

Lecture 4, 19/3/2021 “BP for Graph coloring and Potts models”

Homework 4, due on Wed 31.3: Exercise 5.1 (Bethe free entropy for coloring), Exercise 5.3 (fully connected limit) & Exercise 5.3 (BP for matching). Please upload your homework on Moodle .

Download here the notebook with the plots from Chapter 5.

Lecture 5, 26/3/2021 “Denoising and Optimal Bayesian Inference”

Lecture 6, 1/4/2021 “rank-one matrix factorization”.

Homework 5, due on Wed 14.4 (no delay allowed): Exercise 7.1 (THe BBP transition) & Exercise 7.2:(More phase transitions). Please upload your homework on Moodle .

Lecture 7, 16/4/2021 “Cavity method and AMP”

Homework 6, due on Wed 21.4 (no delay allowed): Exercise 8.1. (Coding AMP for exos 7.1 and 7.2) Please upload your homework on Moodle .

Lecture 8, 23/4/2021 “Stochastic Block Model and Community detection”

Homework 7, due on Wed 28.4 (no delay allowed): Exercise 9.1. and 9.2 Please upload your homework on Moodle .

Lecture 9, 30/4/2021 “Graph Coloring II”

Lecture 10, 7/5/2021 “graph coloring iii”.

Homework 8, due on Wed 19.5 (no delay allowed): Exercise 11.1 (Random subcube model). Please upload your homework on Moodle .

Lecture 11, 15/5/2021 “Replica Symmetry Breaking and the Random Energy Model”

Homework 9, due on Wed 26.5 (no delay allowed): Exercise 12.2 (Second moment of the participation ratio) (after you look at section 12.2.4 of the notes) OR Exercise 12.1 (REM as a p-spin model). Please upload your homework on Moodle .

Lecture 12, 21/5/2021 “Linear problems, LASSO and Approximate Message Passing”

Homework 10, due on Wed 2.6 (no delay allowed): Exercise 13.1 (coding AMP for compressed sensing). Please upload your homework on Moodle .

Online People Directory

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Mathematics and Statistics

Thegradcafe mathematics statistics and statistician forum covers many topics such as best programs, PhDs and more. See others admission results, questions or share your advice with other students!

3,510 topics in this forum

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Before you start agonizing over your personal/research statement for stat or biostat, read this.

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• September 4, 2022

My two cents tips on PhD application for STAT

• statisticsphd
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2025 PhD Stats/OR application, need to find safety schools

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2024 Application for PhD Statistics Programs

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2024 Applicant Profiles and Admission Results for Statistics/Biostatistics

Can you evaluate my profile for statistics phd, profile eval: non-traditional ph.d stats applicant, nontraditional applicants for biostatistics or statistics fall 2024.

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Biostats more or Stats more? Target/Reach/safety?

Stats/ml phd application strategy review - fall 2025, need brutal, honest feedback on application for fall 2025 phd in statistics, recommendations for former math student switching to stats particularly interested in uncertainty quantification, compressed sensing / signal detection, machine learning, manifold learning., stats/biostats phd citizenship, no idea what schools are reach/target/safety + feeling lost, profile review -- seeking advice on what programs i could realistically get into as an applied math major, recommendations for phd in stats/biostats with research in biology and/or agriculture, fall 2025 stats phd profile, reputations of statistics phds - umbc, umd - stat, umd - amsc (applied stat), 2025 biostatistics phd application.

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Florent Krzakala

Full professor, école polytechnique fédérale de lausanne.

Florent Krzakala is a full professor at École polytechnique fédérale de Lausanne in Switzerland. His research interests include Statistical Physics, Machine Learning, Statistics, Signal Processing, Computer Science and Computational Optics. He leads the IdePHIcs “Information, Learning and Physics" laboratory in the Physics and Electric Engineering departments in EPFL. He is also the founder and scientific advisor of the startup Lighton .

• Statistical Physics
• Machine learning
• Probability and Statistics
• Computer Science
• Information theory
• Inference on graphs
• Random Constraint Optimization
• Computational optics

Postdoc, 2004

Roma La Sapienza

PhD in Statistical Physics, 2002

Orsay, Paris XI, France

MSc in Physics, 1999

Orsay, France

Meet the Team

Phd students, luca arnaboldi, yatin dandi, davide ghio, matteo vilucchio, damien barbier, pierre mergny, ludovic stephan, former members (and where to find them), alia abbara, epfl, lausanne, antoine baker, jean barbier, trieste, ictp, francesco caltagirone, research engineer, jonathan dong, angelique dremeau, assoc. professor, laura foini, cnrs, ipht saclay, marylou gabrie, ecole polytechnique, paris, cedric gerbelot, nyu, new york, courant instructor, sebastian goldt, sissa, trieste, alejandro lage, university la havana, cuba, julien launay, researcher at huggingface, bruno loureiro, ens and cnrs, paris, antoine maillard, ethz, zurich, hermann-weyl instructor, andre manoel, ruben ohana, simons center, new york, boshra rajaei, sadjad univ., maria refinetti, g-research, london, google deepmind, luca saglietti, bocconi university, milan, christophe schulke, phillips research, gabriele sicuro, universita di bologna, renmin university, china, eric tramel, rodrigo veiga, institute of theoretical physics, beijing, recent classes, books and reviews, 👋 welcome to the group.

Take a look at what we’re working on…

Learning Neural Nets (with Neural Nets)

Invited talk in the Physics for Neural Network Conference in Princeton, April 2023

See the talk

How do two-layer neural networks learn complex functions from data over time?

Invited talk at Mathematics of Modern Machine Learning in Neurips 2023

Are Gaussian Data All You Need?

Invited talk in the GRAMSIA conference in Harvard, May 2023

Comment fonctionne l’inteligence artificielle ?

Conference grand public de vulgarisation a Cargese, Juillet 2024

Voir la conference

Statistical Physics and Learning

Invited talk in the Turing Institute in London, 2019

Youtube Channel

Videos of talks from Prof. Krzakala

• [email protected]
• +41 21 693 31 87
• EPFL, IdePHICS lab, ELD 239, Station 11, Lausanne, 1015

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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 in Multiscale Granular Mechanics

The Data-Driven Mechanics Laboratory is seeking a highly motivated doctoral student to study the mechanics of granular materials. The goal of the project is to develop a new paradigm by leveraging advanced micromechanical simulation, concepts from configurational mechanics and tools for the data-driven discovery of coarse-grained non-equilibrium thermodynamics. The framework will be used to shed light on the structural evolution of granular materials, transforming our understanding of a wide range of physical systems. This investigation will be complemented by experiments on 3D printed granular materials.

• Perform original research in the field of granular mechanics.
• Disseminate research output in journals and international conferences.
• Contribute to lab activities (including teaching assistance and co-supervision of student projects).
• Strong interest in granular and solid mechanics
• Master’s degree in Civil/Mechanical Engineering, Applied Mechanics, Physics, or related areas.
• Good computer coding skills and experience in low-level (C,C++) languages.
• Knowledge of high-performance computing is an advantage.
• Experience with 3D-printing and experimental techniques is an advantage.
• Excellent English communication skills (oral and written).
• World-class multi-cultural environment on the shores of beautiful Lake Leman
• Innovative interdisciplinary research at the Data-Driven Mechanics Lab.
• Competitive salary and employment conditions.

Contract Start Date :  

Activity Rate Min : 100.00 

Activity Rate Max : 100.00 

Contract Type: CDD

Duration: 4 years (1-year fixed-term contrat renewable annually according to EPFL rules)

Expected start date : January/February 2025

Reference: 1015 

Interested applicants should upload their CV, transcripts of records     (Bachelor’s/Master’s)     and a motivation statement, until September 15th 2024. Applications will be     evaluated in the order that they are received. To be eligible for a PhD at EPFL,     candidates also need to enrol in one of EPFL’s Doctoral School programs. This is a     separate application process that you can start in parallel. We suggest that you submit         your application to the EDCE Doctoral School until September 15th 2024,     indicating     Prof. Karapiperis as the thesis director.   For inquiries please contact Prof.     Kostas Karapiperis at [email protected].

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Coursebooks

Biostatistics

MATH-449 / 5 credits

Teacher: Stensrud Mats Julius

Language: English

This course covers statistical methods that are widely used in medicine and biology. A key topic is the analysis of longitudinal data: that is, methods to evaluate exposures, effects and outcomes that are functions of time. While motivated by real-life problems, some of the material will be abstract

• Likelihood functions for censored data
• Martingales
• Identification of parameters with a clear interpretation
• Non-parametric and semi-parametric estimators
• Discrete vs continuous time
• Parametric regression models
• Semi-parametric models
• Description, Prediction and Causal inference
• Sensitivity analyses
• Identification and estimation of optimal regimes
• Optimal time-varying treatment regimes

Biostatistics; statistical inference; survival analysis; longitudinal data; research synthesis

Learning Prerequisites

Required courses.

The students are expected to have taken introductory courses in statistical theory, probability theory and regression modeling.

Recommended courses

Undergraduate courses in statistics.

Important concepts to start the course

Likelihood theory, statistical testing. Experience with R is an advantage, but is not required.

Learning Outcomes

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

• Identify statistical methods that are suitable for answering a given scientific problem.
• Justify why a statistical method is applied to given problem.
• Apply methods that have been taught in the course.
• Critique evaluate published studies and methodologies.

Transversal skills

• Communicate effectively with professionals from other disciplines.
• Access and evaluate appropriate sources of information.
• Demonstrate the capacity for critical thinking

Teaching methods

Classroom lectures, where I will use Beamer slides and the blackboard. Exercises and take-home projects that will require programing in R.

Assessment methods

Final written exam and continuous assessment.

Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.

Supervision

Virtual desktop infrastructure (VDI)

Bibliography.

Teaching resources

• Aalen, O., Borgan, O. and Gjessing, H., 2008. Survival and event history analysis: a process point of view. Springer
• Andersen, P.K., Borgan, O., Gill, R.D. and Keiding, N., 2012. Statistical models based on counting processes. Springer

Ressources en bibliothèque

• Andersen Statistical models
• Aalen survival and event history

Moodle Link

• https://go.epfl.ch/MATH-449

In the programs

• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Biostatistics
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Type: optional

Reference week

Exercise, TP

Project, other

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A revealed preference analysis of PhD students’ choices over employment outcomes

We develop a revealed preference approach to elicit science and engineering PhDs’ preferences over employment outcomes, exploiting cohort size variations. Depending on whether pecuniary and non-pecuniary rewards are sticky or not, increments in the PhDs’ cohort size decrease either the availability of their ideal employment categories or the related compensations. In both cases, the PhDs’ preferred employment categories are revealed to be the ones that are relatively less chosen when the PhDs’ cohort is large and relatively more so when it is small. Examining two major European universities, we find that PhDs equally value employment in highly-ranked universities and R&D-intensive companies. Moreover, these employment categories are preferred to low-ranked universities, non-R&D-intensive firms, and public administration. There is preference heterogeneity across PhDs depending on their research field.

WOS:000364620700011

Revealed preferences

Employment choices

PhD students

Cohort size effects

Federal Institute of Technology Lausanne: Statistics

Updated: February 29, 2024

Quick Review

* Federal Institute of Technology Lausanne is among the institutions that don't provide data on acceptance rates. This might happen because the university has programs where applicants only need to meet admission requirements to enroll and don't necessarily compete with others.

We estimate the above acceptance rate based on admission statistics of closely ranked nearby universities with similar research profiles that do publish such data.

Acceptance rate & Admissions

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Federal Institute of Technology Lausanne is a world-class research university with 128,513 scientific papers published and 4,890,119 citations received. The research profile covers a range of fields, including Engineering, Physics, Chemistry, Quantum and Particle physics, Biology, Computer Science, Materials Science, Environmental Science, Organic Chemistry, and Liberal Arts & Social Sciences.

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Federal Institute of Technology Lausanne alumni

Mattia Binotto

Mattia Binotto is a Swiss-born Italian engineer and the former team principal of Scuderia Ferrari in Formula One. He was appointed to the role on 7 January 2019, replacing Maurizio Arrivabene. His parents are Italian.

Leila Hatami

Leila Hatami is an Iranian actress. Born to filmmaker Ali Hatami and actress Zari Khoshkam, she began her career with acting in the films of her father. She rose to international fame for her role as Simin in Asghar Farhadi's Academy Award-winning film A Separation (2011), for which she received the Silver Bear for Best Actress.

Joachim Son-Forget

Joachim Jean-Marie Forget, known as Joachim Son-Forget is a South Korean-born French politician. Holding a doctorate in neuroscience, he also works part-time as a radiologist in Switzerland. He has held Kosovar citizenship since 2018.

Othman Benjelloun

Othman Benjelloun is a Moroccan banker billionaire businessman. He is known for co-founding of BMCE Bank and Bank of Africa, and serves as its chairman, chief executive officer. In February 2022, his net worth was estimated by Forbes at US$1.6 billion.

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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).

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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.

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