• 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

• Excellent support during the academic qualification phase
• Structured doctoral degree programs
• Systematic career development in collaboration with the Graduate Center
• Thorough onboarding process with a dedicated team
• Joint team activities
• Subsidized food and drinks in our student restaurants
• Place of work within comfortable walking distance of public transport
• Family-friendly environment with childcare options, also during school holidays
• Flexible working hours
• A wide range of training courses and opportunities for professional development
• Active health management

Position Details

DEADLINE: Wed. February 11, 2026

• Duration: Initially limited to up to two (2) years, with the possibility of extension.
• Starting Date: To be adapted to the availability of selected candidates (preferably before March 1, 2026).
• Salary: Competitive international annual gross salary following the German TV-L (A13 / E 13) scale.
• Location: The position is based in Erlangen, Bavaria, Germany.

While the primary focus is on research, collaboration with the Chair activity and mentoring is required. The position is funded by the state of Bavaria and comes with some teaching duties.

Your profile

Desirable 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 and written)

Topics of interest (examples)
• PDE-constrained optimization and control
• Data-driven modeling for dynamical systems and PDEs
• Learning-based numerical methods and operator learning
• Structure-preserving numerical schemes, inverse problems, uncertainty quantification
• Connections between control, reinforcement learning, and scientific ML

HOW TO APPLY

Deadline: Wed. February 11, 2026

Interested candidates are invited to submit their applications via email to dcn-jobs[at]fau.de providing the following information:

a) Cover Letter:
• Brief description of the topic and results of your PhD thesis.
• Brief description of your previous postdoctoral activities (if applicable).
• Description of your expectations for the PhD/postdoctoral position in our research group.

b) Curriculum Vitae:
• Including a list of publications and preprints.

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_ERCassis2026_candidateNameLastname.pdf) with the required information via email to dcn-jobs[at]fau.de with the following information
* Subject of Email: FAU Postdoc 2026
Applications will be reviewed on a rolling basis, and shortlisted candidates will be invited for an interview, either in person or online.

SEE this call at FAU DCN-AvH's website: dcn.nat.fau.eu/careers