Postdoctoral Researcher in Applied Mathematics Arbeitszeit: Vollzeit: 40 hours/week Einstellungstermin: 01.01.2027

Postdoctoral Researcher in Applied Mathematics Arbeitszeit: Vollzeit: 40 hours/week Einstellungstermin: 01.01.2027 Bewerbungsschluss: 30.10.2026

Postdoctoral Researcher in Applied Mathematics

Control, Dynamical Systems and Machine Learning

ERC Advanced Grant CoDeFeL Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) 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 a postdoctoral researcher position in applied mathematics within the ERC Advanced Grant CoDeFeL – Control for Deep and Federated Learning. We seek outstanding researchers with strong expertise in an area of applied mathematics, including partial differential equations, control theory, dynamical systems, optimization, numerical analysis, scientific computing or the mathematical foundations of machine learning. Strong expertise in one relevant area is sufficient. Prior experience combining control and machine learning is not required. The position offers the opportunity to develop your own research and explore new connections with control, dynamical systems and machine learning. Interdisciplinarity can grow through collaboration within the group.

Benefits: We Have a Lot To Offer

• Regular promotion to the next level and increase in salary pursuant to the collective bargaining agreement for the public service of the German Länder (TV-L) or remuneration pursuant to the Bavarian Public Servants Remuneration Act (BayBesG) plus an additional annual bonus
• 30 days annual leave at five working days per week with additional free days on December 24 and 31
• Occupational pension scheme and asset accumulation savings scheme

Position Details

• Employment : Full-time • Duration : Two years initially, with the possibility of extension • Application Deadline : Friday, October 30, 2026 • Starting Date : Flexible, preferably before 1 January 2027 • Salary : German public-sector salary scale TV-L E13 • Location : Erlangen – Bavaria, Germany The position is strongly research-oriented, with participation in the scientific activities of the ERC team and the Chair.

Scientific Environment

The successful candidate will join the FAU DCN-AvH Chairand interact closely with the FAU MoD Research Center for Mathematics of Data. The group offers an international research environment connecting control theory, applied mathematics, partial differential equations (PDEs), scientific computing, optimization and machine learning. CoDeFeL – Control for Deep and Federated Learning is an ERC Advanced Grant devoted to developing mathematical foundations for modern learning systems through ideas from control, dynamics, optimization and applied mathematics.

The successful candidate will have substantial freedom to develop an independent research programme in applied mathematics. We welcome researchers who bring their own questions, methods and expertise and wish to explore connections with the scientific programme of CoDeFeL.

The ERC project provides a broad intellectual framework, an active research team, international collaborations and resources for ambitious research. It is not intended to prescribe a narrowly defined postdoctoral project. The successful candidate will be encouraged to: • Develop original theoretical and/or computational research • Initiate new research directions and collaborations • Interact with researchers across the Chair and FAU MoD • Participate in international scientific activities associated with the group and the ERC project • Publish and present research results internationally • Contribute to the intellectual development of junior researchers The objective is to strengthen the candidate’s independent scientific profile and future academic career, building on their own mathematical strengths.

Qualifications

Applicants should have: • A PhD in mathematics, applied mathematics, computer science, machine learning or a closely related field • A strong research record and clear potential for independent research • Substantial expertise in at least one relevant area of applied mathematics or the mathematical foundations of machine learning • Interest in exploring connections between your research and control, dynamical systems or machine learning • A good command of English • The ability to work independently and collaboratively in an international research environment Computational experience is welcome for candidates pursuing computational research but is not required for applicants whose research profile is primarily theoretical.