Mathematics is a fundamental discipline that serves as the language of science and a precise tool for reasoning about the world. At EPFL, research in mathematics is broad, encompassing fundamental areas like algebra, analysis, geometry, probability, and topology, as well as applied fields such as numerical analysis, optimization, statistics, and data science. This PhD program provides a strong academic environment, advanced coursework, and training designed to equip doctoral candidates with advanced mathematical knowledge and research skills. Graduates are prepared for careers in academia, industrial research, finance, data science, engineering, and education, where strong quantitative and analytical abilities are highly valued.
Graduates from the EDMA program are well-prepared for diverse career paths. Many pursue academic careers as postdocs and professors, while others excel in industrial research, finance, data science, engineering, and education. EPFL fosters innovation through strong ties to industry, start-ups, and academic institutions, equipping graduates with essential analytical skills for impactful roles.
Applicants are encouraged to review the eligibility criteria and research areas to align their application with available supervisors and projects. Visiting the EPFL website for detailed admission criteria and application procedures is recommended.
While tuition is free, students should budget for living expenses. Specific costs may vary based on individual lifestyle and accommodation choices.
Tuition fees for PhD candidates at EPFL are CHF 0 per year. Students should budget for living expenses, estimated at CHF 20,000 per year.
Applicants must have a strong academic record and a Master's degree in Mathematics or a related field. It is recommended to review the EPFL website for detailed admission criteria and application procedures.
Applications are submitted online and can be done three times a year with deadlines on April 1, September 1, and December 1. You will need to submit an online form, academic transcripts, CV, motivation letter, and letters of recommendation.
Graduates are prepared for careers in academia (Postdoctoral Researcher, Professor), industrial research, data science, finance, engineering, and education.
The provided data does not specify the language of instruction.