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École Polytechnique Fédérale de Lausanne

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Postdoc Position in Quantitative Finance and Applied Machine Learning at EPFL École Polytechnique Fédérale de Lausanne in Switzerland

Degree Level

Postdoc

Field of study

Computer Science

Funding

Competitive salary and excellent working conditions are offered. The appointment is a 1-year contract renewable; no tuition information is mentioned.

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Country

Switzerland

University

École Polytechnique Fédérale de Lausanne

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Keywords

Computer Science
Mathematics
Finance
Quantitative Finance
Economics
Statistics
Study Research
Post Doc

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About this position

EPFL (École Polytechnique Fédérale de Lausanne) is recruiting a Postdoc Position in Quantitative Finance within the Swissquote Chair in Quantitative Finance. The research focus is quantitative finance with an emphasis on applied machine learning, and the exact topic will be discussed with the selected candidate based on background, expertise, and interests.

The postdoc will work on research projects, analyze and publish results, build a research network, and contribute to education as well as supervision of PhD and master students. The role is based at EPFL in Lausanne, Switzerland, in a highly international and research-intensive environment.

This is a postdoctoral opening, not a PhD or master’s call. The contract is 1 year, renewable, with a competitive salary and excellent working conditions. No tuition waiver or scholarship programme is mentioned.

How to apply: applications are accepted only through the online platform. Prepare a brief cover letter (up to 2 pages), a CV with publication list, a research statement (up to 3 pages), and contact details for 3 referees. For questions, contact Ms Sophie Kauz-Cadena at [email protected].

Funding details

Competitive salary and excellent working conditions are offered. The appointment is a 1-year contract renewable; no tuition information is mentioned.

What's required

A relevant doctoral background is implied for a postdoc position. Candidates should have research experience in quantitative finance and applied machine learning, with the ability to work on research projects, publish results, build research networks, and contribute to education and student supervision. Applicants must submit a cover letter, CV with publication list, research statement, and contact details for three referees.

How to apply

Apply only through the online platform. Submit a brief cover letter, a CV with publication list, a research statement, and contact details for three referees.

More information can be found here

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