ERC Advanced Grant CoDeFeL project (02) Postdoc positions in “Control and Machine Learning”, Erlangen (Germany)
The “Chair for Dynamics, Control, Machine Learning, and Numerics – Alexander von Humboldt Professorship (FAU DCN-AvH)”, led by Prof. Enrique Zuazua, invites applications for two (02) open postdoctoral research positions at the ERC CoDeFeL, Control for Deep and Federated Learning project.
Context of the ERC-CoDeFeL project
CoDeFeL seeks to make a breakthrough that takes the mathematical foundations of Machine Learning beyond their present frontiers, through the systematic development of new ideas and methods inspired by control theory. The project is developed in cooperation with the University of Deusto in Bilbao, Basque Country, Spain.
Position Details
• Duration: Initially limited to up to three (3) years, with the possibility of extension.
• Starting Date: To be adapted to the availability of selected candidates (preferably on February 1, 2025).
• Salary: Competitive international annual gross salary following the German TV-L (E 13) scale.
• Location: The positions are based in Erlangen, Bavaria, Germany.
We are looking for a postdoctoral researcher with expertise in mathematical Control and/or Machine Learning and numerical Partial Differential Equations. The candidate is expected to be able to address the research challenges of the ERC CoDeFeL project in cooperation with team members and partners, contributing to the supervision of the younger fellows of the team.
Furthermore, the candidate will be involved in publishing research findings in scientific journals, presenting them at national and international conferences, and supporting the research team with the technical reporting and results dissemination of the CoDeFeL project.
While the primary focus is on research, collaboration with the Chair activity and mentoring is encouraged.
Your profile
Requested background knowledge:
• PhD in Applied Mathematics or Machine Learning
• High level/experience in Control and/or Machine Learning
• Proven experience in Partial Differential Equations and Numerical Analysis
• Computational skills to develop computational codes (Python and MATLAB)
• Ability to work independently and collaboratively in an international and interdisciplinary team
• Excellent knowledge of English (oral/written)
Application Procedure
Application Deadline: Interested candidates are invited to submit their applications via email to dcn-jobs[at]fau.de by Wed. January 15, 2025.
Applicants should provide the following information:
a) Cover Letter:
• Brief description of the topic and results of your PhD thesis.
• Brief description of your previous postdoctoral activities.
• Explanation of how your expertise relates to the research topics of the ERC CoDeFeL.
• Description of your expectations from the postdoctoral position in our research group.
b) Curriculum vitae: Including a list of publications and preprints (if any).
c) Reference Information: List of 2-3 professors (with contact information) who can provide a reference letter. Explain your connection to them. No recommendation letters are required at this stage.
d) Tentative Research Proposal: One-page proposal aligned with the ERC CoDeFeL project research scope.
Please send a single PDF file (titled FAU_ERCpostdoc2025_candidateNameLastname.pdf) with the required information via email to dcn-jobs[at]fau.de with the following information
* Subject of Email: FAU ERC CoDeFeL Postdoc 2025
Applications will be reviewed on a rolling basis, and shortlisted candidates will be invited for an interview, either in person or online.
Institutional Values
FAU Erlangen-Nürnberg is committed to international standards, transparent performance agreements, equal opportunity, inclusivity, support for under-represented groups, an inclusive culture, and diversity. The university also prioritizes the needs of dual career couples and is recognized as a family-friendly employer.
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|| See open positions at FAU DCN-AvH Careers page
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The CoDeFeL project (ERC-2022-ADG) has received funding from the European Union’s Horizon ERC Grants programme under grant agreement No. 101096251.
The views and opinions expressed are solely those of the author(s) and do not necessarily reflect those of the European Research Council Executive Agency (ERCEA), the European Union or the granting authority who cannot be held responsible for them.