Stellenausschreibungen

The Chair for Dynamics, Control, Machine Learning, and Numerics -Alexander von Humboldt Professorship (FAU DCN-AvH) led by Professor Enrique Zuazua, invites applications for two (02) Postdoctoral research positions at the ERC CoDeFeL, Control for Deep and Federated Learning project.

Context of the ERC-CoDeFeL project

CoDeFeL seeks to make a breakthrough that takes the mathematical foundations of Machine Learning beyond their present frontiers, through the systematic development of new ideas and methods inspired by control theory.

Position Details

• Duration: Initially limited to up to three (3) years, with the possibility of extension.

• Starting Date: To be adapted to the availability of selected candidates (preferably before August, 2025).

• Salary: Competitive international annual gross salary following the German TV-L (E 13) scale.

• Location: The positions are based in Erlangen, Bavaria, Germany.

See more details at: https://dcn.nat.fau.eu/careers/

Necessary qualifications:

Qualifications required

• 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/written)

Desirable qualifications:

APPLICATION PROCEDURE

Application deadline

Interested candidates are invited to submit their applications (single PDF) via email to dcn-jobs[at]fau.de by Fri. May 9, 2025.

Applicants should provide the following information:

a) Cover Letter:

• Brief description of the topic and results of your PhD thesis

• Brief description of your previous postdoctoral activities

• Explanation of how your expertise relates to the research topics of the ERC CoDeFeL

• Description of your expectations from the postdoctoral position in our research group

b) Curriculum Vitae: Including a list of publications and preprints (if any)

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_ERCpostdoc2025_candidateNameLastname.pdf) with the required information via email to dcn-jobs[at]fau.de with the following information

* Subject of Email: FAU ERC CoDeFeL Postdoc 2025

Applications will be reviewed on a rolling basis, and shortlisted candidates will be invited for an interview, either in person or online.

Institutional Values

FAU Erlangen-Nürnberg is committed to international standards, transparent performance agreements, equal opportunity, inclusivity, support for under-represented groups, an inclusive culture, and diversity. The university also prioritizes the needs of dual career couples and is recognized as a family-friendly employer.

Read more about CoDeFeL project:

https://dcn.nat.fau.eu/codefel/

The CoDeFeL project (ERC-2022-ADG) has received funding from the European Union’s Horizon ERC Grants programme under grant agreement No. 101096251. The views and opinions expressed are solely those of the author(s) and do not necessarily reflect those of the European Research Council Executive Agency (ERCEA), the European Union or the granting authority who cannot be held responsible for them.

The Institute of Geography seeks motivated candidates for six-year position at the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) in Erlangen, Germany. The Institute of Geography seeks motivated candidates for six-year position at the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) in Erlangen, Germany.

CryoSCOPE Research Scientist/Postdoc Position.

The results of this project will help inform and initialise GLOF modelling and may prove useful in model validation. Should the method be globally applicable, this would be useful for risk estimation. Global lake catalogues exist, but none has yet been produced using a consistent automated method, and these catalogues are usually static and quickly outdated; developing such a method would mark a significant advance, particularly with regards to the project’s focus on more easily generating end-user products. Such an automatised remote-sensing-based method would enable much easier detection of GLOFs if lakes suddenly disappeared or drastically shrank between snapshots, which would greatly improve our understanding of this phenomenon.

The position is initially available for 2 years with the possibility to extend for a further 12-18 months, depending on start date, progress and interest. It would suit a recently graduated Masters or PhD student with relevant experience in remote sensing and image processing, as well as deep learning, Due to the relatively narrow focus of this position, however, it is not envisaged that this will lead directly to a PhD if the applicant does not already possess one.

The successful candidate would be based at the Institute for Geography at FAU Erlangen-Nürnberg as part of the existing well-established glaciological research group at the institute, with particular strengths in remote sensing, ice-flow modelling, and the application of deep learning in a glaciological context. The supervisor will be Dr Samuel Cook along with another PI in the department or the CryoSCOPE project, to be confirmed based on the profile and interests of the candidate. The salary will depend on the experience of the candidate. Ideally, the candidate will be available to start immediately, but this is flexible.

FAU is the top university for glaciological research in Germany and is split across the 18th-century university town of Erlangen and the historically important city of Nürnberg in the state of Bavaria. The wider region is a major climbing destination, with several major cultural attractions in easy reach. The Alps are only a couple of hours away.

Befristetes Forschungsvorhaben

The results of this project will help inform and initialise GLOF modelling and may prove useful in model validation. Should the method be globally applicable, this would be useful for risk estimation. Global lake catalogues exist, but none has yet been produced using a consistent automated method, and these catalogues are usually static and quickly outdated; developing such a method would mark a significant advance, particularly with regards to the project’s focus on more easily generating end-user products. Such an automatised remote-sensing-based method would enable much easier detection of GLOFs if lakes suddenly disappeared or drastically shrank between snapshots, which would greatly improve our understanding of this phenomenon.

The position is initially available for 2 years with the possibility to extend for a further 12-18 months, depending on start date, progress and interest. It would suit a recently graduated Masters or PhD student with relevant experience in remote sensing and image processing, as well as deep learning, Due to the relatively narrow focus of this position, however, it is not envisaged that this will lead directly to a PhD if the applicant does not already possess one.