Postdoctoral Researcher in Applied Mathematics

Research directions We welcome researchers with strong expertise in one or more of the following areas who are interested in exploring connections with the scientific programme of CoDeFeL: • Control theory and dynamical systems, including the analysis and control of nonlinear and complex systems • Partial differential equations and mathematical analysis, including transport equations and mean-field methods • Optimization and the calculus of variations, including long-time dynamics and turnpike phenomena • Numerical analysis and scientific computing, including methods for PDEs, control and optimization • Mathematical foundations of machine learning, particularly through control, dynamics and analysis • Scientific machine learning, including physics-informed methods, operator learning and learning for dynamical systems and PDEs • Hybrid modelling, combining mechanistic understanding with data-driven methods • Neural differential equations and deep learning architectures, including the analysis, control and optimization of ResNet-type models • Federated and distributed learning, including convergence, communication efficiency and privacy • Robust and adaptive learning and control, including real-time learning and optimization • Mathematical foundations of large language models, including approximation, optimization and dynamical perspectives These directions illustrate the research environment rather than define a checklist of requirements. Strong expertise in one relevant area is sufficient. We welcome both established interdisciplinary profiles and excellent specialists interested in developing new connections. Applicants are encouraged to bring their own questions and build an independent research agenda within the broad framework of CoDeFeL. Who Should Apply We welcome excellent specialists in PDEs, optimization and the calculus of variations, numerical analysis and scientific computing, control theory and dynamical systems, mathematical or scientific machine learning, and related areas of applied mathematics. Candidates may already work across these subjects or be interested in exploring connections from a strong disciplinary base. Previous research at the intersection of control and machine learning is not a prerequisite. For example, a PDE analyst, an optimization researcher or a numerical analyst interested in questions arising from dynamics, control or learning would be a natural candidate. The position offers scope to broaden your research while strengthening your established expertise and scientific identity. Scientific Freedom and Career Development 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. Why Join Us Combine an independent research agenda with the environment of an Alexander von Humboldt Professorship, the resources and international network of an ERC Advanced Grant, and the breadth of FAU Mathematics of Data. The position offers opportunities for international collaboration, scientific visits, workshops and conference participation, bringing together researchers whose different strengths can generate new interdisciplinary directions. How to Apply Please email your application to dcn-jobs[at]fau.de by October 30, 2026. Submit the following documents as a single PDF. 1 Curriculum Vitae Include your publications and preprints. 2 Short research statement Maximum 2 pages. Briefly describe your main research contributions, the questions that currently interest you, and directions you might like to pursue at FAU, starting from your own expertise. A complete research proposal or prior work combining control and machine learning is not required. You need not cover all CoDeFeL topics. Existing research statements prepared for other applications may be adapted and submitted. 3 Brief motivation Maximum half a page. Explain why the scientific environment of the Chair and/or CoDeFeL could be relevant to your research. 4 References Provide the names and contact details of 2–3 senior researchers who may provide references. No recommendation letters are required at the initial application stage. Submission details PDF filename FAU_CoDeFeL_Postdoc2026_NameLastname.pdf Email subject FAU Postdoc 2026 – ERC CoDeFeL Applications will be reviewed on a rolling basis. Shortlisted candidates will be invited for an informal scientific discussion and interview, online or in person.