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Doctoral Research Associate (Wissenschaftlicher Mitarbeiterin, salary level E 13 TV-L)

Universität Münster -
Münster

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Details zur Stelle

Teilzeit | Vollzeit
vor 10 Tagen

Qualifikationen

TensorFlow
Signalverarbeitung
Datenanalyse
Forschungserfahrung
Master
C++
C
Bachelor
Machine Learning
Deep learning
Physik
Künstliche Intelligenz
Python

Vollständige Stellenbeschreibung

Creating knowledge. Making a difference. Shaping the future. This is what the University of Münster stands for as one of the strongest research-oriented universities in Germany and one of the largest employers in the region. Every day, over 7,000 employees devote themselves to cutting-edge research and excellent teaching. We share more than a workplace. We share the desire to make a real impact – regionally and internationally. Our day-to-day work is distinguished by diversity, openness and space to pursue one’s ideas. Become a part of our team – in academia, a technical or manual trade, or administration.

The Institute for Nuclear Physics in the Faculty of Physics at the University of Münster, Germany, is seeking to fill the position of a

**Doctoral Research Associate (Wissenschaftlicher Mitarbeiterin, salary level E 13 TV-L)**

We are offering a part-time position (67% FTE) as a Doctoral Research Associate at the earliest possible date. The position is fixed-term for 3 years. Full-time employees are required to teach 4 hours per week during the semester.

Your tasks:

Become part of the BMFRT-funded joint research project Newtonian Noise Cancelling Headphones and work on machine-learning-based noise mitigation pipelines, in particular low-latency spatio-temporal seismic forecasting and noise subtraction, for underground gravitational-wave detectors such as the Einstein Telescope (ET), a planned third-generation European flagship project.

Development and training of real-time, multi-station seismic forecasting and noise subtraction models based on state-of-the-art deep learning architectures;
Investigation of advanced spectral representations, frequency-aware embeddings, and multi resolution waveform representations to enhance predictive performance in the low frequency regime relevant for seismic and Newtonian noise mitigation;
Analysis and optimisation of autoregressive stability, computational efficiency, and inference latency to meet the real-time requirements of ET veto and noise-subtraction pipelines;
Systematic benchmarking of machine-learning-based forecasting against classical Wiener filtering approaches;
Design of low-latency neural architectures suitable for real-time deployment, establishing performance trade-offs between sensor density, computational complexity, and achievable noise mitigation;
Preparation of reports, presentations, and scientific publications of research results in journals and at conferences;
The position is tied to working towards a doctorate;
Participation in teaching and supervision of students (bachelor’s and master’s theses) in related research topics.

Our expectations:

A completed university degree (master’s or equivalent) in physics, geophysics, computer science, or a related field;
Experience in AI, machine learning, and optimisation methods (e.g. deep learning, Bayesian optimisation, reinforcement learning, surrogate modelling, or related approaches);
Good programming skills in Python and/or C/C++; experience with modern deep learning frameworks (e.g. PyTorch, TensorFlow, or JAX) is an advantage;
Experience in numerical simulations (e.g. wave propagation, finite element modelling, or comparable computational methods) is an advantage;
Good written and spoken English skills for collaboration in an international research team;
Knowledge of signal processing, spectral analysis, or time-series analysis is an advantage;
Experience with seismic data analysis or seismic simulation tools (e.g. AxiSEM, SPECFEM3D, ObsPy) is an advantage;
A high degree of motivation, initiative, and scientific curiosity, as well as the ability to work both independently and in an interdisciplinary team;
Strong communication and collaboration skills.

Advantages for you:

Appreciation, commitment, openness and respect – values which are important to us.
If you have family members or young children in your care, our Family Service Office offers concrete support to help you balance your private and professional responsibilities.
As an educational institution, we are deeply committed to offering occupational training and continuing education opportunities tailored to your individual needs.
As a university employee, you are entitled to numerous benefits afforded to public servants, e.g. an attractive company pension scheme (VBL), an annual end-of-year bonus and a position that is shielded from economic fluctuations.

The University of Münster strongly supports equal opportunity and diversity. We welcome all applicants regardless of sex, nationality, ethnic or social background, religion or worldview, disability, age, sexual orientation or gender identity. We are committed to creating family-friendly working conditions.

We actively encourage applications by women. Women with equivalent qualifications and academic achievements will be preferentially considered unless these are outweighed by reasons which necessitate the selection of another candidate.

If you have any questions, please contact Dr. Waleed Esmail (+49 (0) 251 8334947 / [email protected]) or Prof. Dr. Alexander Kappes ((+49 (0) 251 8334996 / [email protected]).

Are you interested? Then we look forward to receiving your application (motivation letter, CV, master certificate / transcript of records, 2 contacts for reference letters) by 2026-08-15 at:

Universität Münster
Institut für Kernphysik
Prof. Dr. Alexander Kappes
Wilhelm-Klemm-Str. 9
48149 Münster

You may also send us your application electronically in PDF format to [email protected].
Please note that we cannot consider other file formats.

Reference number: 2026_07_19

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