The Chair for Dynamics, Control, Machine Learning, and Numerics – Alexander von Humboldt Professorship (FAU DCN-AvH) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany, is seeking applications for a Postdoctoral Research Assistant position. The research group, led by Professor Enrique Zuazua, focuses on cutting-edge topics at the intersection of partial differential equations (PDEs), numerical analysis, control, and machine learning. The successful candidate will join an international and interdisciplinary environment, collaborating with the FAU MoD Research Center for Mathematics of Data. The position is full-time, initially for two years with the possibility of extension, and is funded by the state of Bavaria. The salary is competitive, following the German TV-L (A13/E13) scale. The role is primarily research-oriented, with some teaching duties and no administrative responsibilities. Research topics include PDE-constrained optimization and control, data-driven modeling for dynamical systems and PDEs, learning-based numerical methods, operator learning, structure-preserving numerical schemes, inverse problems, uncertainty quantification, and connections between control, reinforcement learning, and scientific machine learning. Applicants must have a PhD in Applied Mathematics, Machine Learning, or a closely related field, with strong expertise in control and/or machine learning, proven research experience in PDEs and numerical analysis, and solid computational skills (e.g., Python, MATLAB). Excellent English communication skills and the ability to work both independently and collaboratively are essential. The group values diversity, inclusivity, and equal opportunity, and supports dual career couples and family-friendly policies. To apply, candidates should submit a single PDF file containing a cover letter (with a brief description of their PhD thesis and previous postdoctoral activities, if applicable, and expectations for the position), a CV with a list of publications, reference information for 2-3 professors, and a one-page research proposal aligned with the ERC CoDeFeL project. Applications should be sent via email to [email protected] with the subject 'FAU Assistant 2026'. The deadline for applications is February 11, 2026, and applications will be reviewed on a rolling basis. Shortlisted candidates will be invited for an interview. For more information, visit the FAU DCN-AvH Careers page or the official call .

The Chair for Dynamics, Control, Machine Learning, and Numerics – Alexander von Humboldt Professorship (FAU DCN-AvH) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany, is seeking applications for a postdoctoral researcher position in the areas of partial differential equations (PDEs), control, and machine learning. The successful candidate will join an internationally recognized research group led by Professor Enrique Zuazua, working at the interface of PDE analysis, control, scientific computing, and machine learning. The research environment is highly interdisciplinary, with collaborations across the FAU MoD Research Center for Mathematics of Data. The position is full-time, initially for two years with the possibility of extension, and is funded by the state of Bavaria. The salary is competitive and follows the German TV-L (A13/E13) scale. The role includes some teaching duties but is primarily focused on scientific research, with no administrative responsibilities. Research topics of interest include PDE-constrained optimization and control, data-driven modeling for dynamical systems and PDEs, learning-based numerical methods, operator learning, structure-preserving numerical schemes, inverse problems, and uncertainty quantification. The group also explores connections between control, reinforcement learning, and scientific machine learning. Applicants should have a PhD in Applied Mathematics, Machine Learning, or a closely related field, with strong expertise in control and/or machine learning, proven research experience in PDEs and numerical analysis, and solid computational skills (e.g., Python, MATLAB). Excellent English communication skills and the ability to work both independently and collaboratively in an international environment are essential. The university values diversity, equal opportunity, and inclusivity, and supports dual career couples and family-friendly policies. To apply, candidates should submit a single PDF file containing a cover letter (with a brief description of their PhD thesis and previous postdoctoral activities, if applicable), CV with publications, reference contacts, and a one-page research proposal aligned with the ERC CoDeFeL project. Applications should be sent by email to [email protected] with the subject 'FAU Assistant 2026'. The application deadline is February 11, 2026, and applications are reviewed on a rolling basis. Shortlisted candidates will be invited for an interview.