Enrique Zuazua

Prof. at Friedrich-Alexander-Universität Erlangen-Nürnberg

Friedrich-Alexander Universität Erlangen-Nürnberg
Country flag
Germany

Research Interests

Explore related searches

About

Professor Enrique Zuazua is a faculty member at Friedrich-Alexander-Universität Erlangen-Nürnberg in Germany. His research primarily focuses on control theory, dynamical systems, and their applications in various mathematical equations, as evidenced by his recent publications on topics such as the heat equation, wave equation, and nonlocal Burgers' equations. He also explores innovative methodologies in federated learning and optimal actuator design, contributing significantly to the field of applied mathematics.

Recent Grants

Grant: Close

DyCon: Dynamic Control Project

Open Date:

Close Date:

Grant: Close

FP7-246775 - NUMERIWAVES

Open Date:

Close Date:

Grant: Close

CoDeFeL: Control and Deep and Federated Learning

Open Date:

Close Date:

Positions (3)

Publisher
source

Enrique Zuazua

University Name
.

Friedrich-Alexander-Universität Erlangen-Nürnberg

Postdoctoral Research Assistant in PDEs, Control, and Machine Learning at Friedrich-Alexander-Universität Erlangen-Nürnberg

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 .

8 months ago

Publisher
source

Enrique Zuazua

University Name
.

Friedrich-Alexander-Universität Erlangen-Nürnberg

Postdoctoral Researcher in PDEs, Control, and Machine Learning at Friedrich-Alexander-Universität Erlangen-Nürnberg

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.

8 months ago

Publisher
source

Enrique Zuazua

University Name
.

Friedrich-Alexander-Universität Erlangen-Nürnberg

Postdoctoral Researcher in Applied Mathematics

Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) is advertising a Postdoctoral Researcher in Applied Mathematics within the Chair for Dynamics, Control, Machine Learning, and Numerics (FAU DCN-AvH) in Erlangen, Bavaria, Germany. The position is hosted by the team led by Prof. Enrique Zuazua and is part of the ERC Advanced Grant CoDeFeL — Control for Deep and Federated Learning . The appointment is strongly research-oriented and focuses on applied mathematics topics related to control, dynamical systems, machine learning, numerical analysis, and the mathematics underlying deep and federated learning. This is a full-time postdoctoral post, initially for two years with the possibility of extension. The role is funded through the ERC/Horizon Europe framework and pays according to the German public-sector salary scale TV-L E13 . The expected starting date is flexible, preferably before 1 January 2027 . Applicants should hold a PhD or equivalent in applied mathematics or a closely related field and should have good English proficiency. The post is suitable for early-stage, recognised, or established researchers who want to contribute to an active ERC team and participate in the scientific activities of the chair. The application deadline is 30 October 2026 at 23:59 (Europe/Berlin) . Applications should be submitted through the FAU careers page, and further details are available via the additional job information link.

New Today

Articles (14)

Collaborators (2)

Idriss Mazari

Assistant Professor

Université Paris-Saclay

FRANCE

Yongcun Song

Assistant Professor

Nanyang Technological University

SINGAPORE
Social connections

How do I reach out?

Sign in for free to see their profile details and contact information.

Meet Kite AI