About the opportunity
The Simply Complex Lab at the Faculty of Electrical Engineering and Information Technology, Ruhr-Universität Bochum, is seeking a full-time Research Fellow (m/f/x) to focus on scientific machine learning and AI-microscopy integration. The lab investigates emergent phenomena far from thermodynamic equilibrium at the intersection of soft condensed matter, complexity, and nonlinear physics. As a research fellow, you will develop reinforcement learning and pattern recognition methods to analyze live experimental data streams and actively guide experiments through feedback loops.
Salary and benefits
- Salaried position based on the collective agreement of the Länder (TV-L), pay grade E13 TV-L upon meeting personal and collective agreement requirements
- Full-time employment (39.83 hours/week) for a 12-month project duration
- No teaching obligation if the position is funded by third-party funds
- Access to an established experimental platform and state-of-the-art imaging systems
- Support from a Research Software Engineer and student assistants (HiWi)
- Extensive training and professional development opportunities
- Access to a varied university sports program with around 100 sport disciplines and the university's own gym
- Free German and English language courses provided by the University Language Center (ZFA)
Who can apply?
- An academic degree in Machine Learning, Artificial Intelligence, Computer Science, Physics, Applied Mathematics, Engineering, or a closely related field
- Strong experience in machine learning research
- Experience developing and evaluating reinforcement learning algorithms
- Strong Python programming skills and experience with PyTorch and/or TensorFlow
- Experience working with large datasets and scientific computing
- Excellent scientific communication (written and oral) and collaboration skills
- Competence in at least one of the campus languages (German or English) and willingness to learn the other
Required documents
- Cover letter
- Curriculum vitae
- Certificates (e.g., employment certificates, degree certificates, training certificates)
When to apply
- Applications must be submitted by 2026-11-01
- Position is to be filled at the earliest possible date
How to apply
- Prepare your application documents including a cover letter, CV, and relevant certificates
- Submit your application via the official online application portal referencing reference number ANR 6152
Additional information
- The place of work is Ruhr University Bochum
- The position is fixed-term for 12 months in connection with project duration
- Travel costs, accommodation costs, and loss of earnings for job interviews cannot be reimbursed
- The Staff Council participates in selection interviews upon request
Our Take on This Opportunity
This section reflects Open Opportunities’ own analysis of the opportunity from an applicant’s perspective.
You may be a strong fit if…
- Ideal for researchers with a strong computational background eager to apply machine learning directly to physical sciences and automated experiments.
- Provides an excellent publishing environment with strong prospects for high-impact outputs at the crossroads of AI and advanced imaging.
- Suits candidates seeking a collaborative academic setting with access to dedicated research software engineering and student support.
What should you think carefully about?
- The role is offered on a fixed 12-month contract aligned with project duration.
- Applicants must be comfortable relocating to Bochum, Germany, or commuting locally as on-site presence is required.
- Language flexibility is necessary given the bilingual campus environment requiring willingness to learn German or English.
How could this fit your current goals and plans?
This 12-month research fellowship offers an intensive opportunity to bridge machine learning theory with physical experiments, making it a powerful stepping stone for researchers aiming for academic leadership or advanced roles in scientific AI.
Our take
Early-career and experienced researchers specialized in reinforcement learning, scientific machine learning, or complex physics should seriously consider applying before the November 1, 2026 deadline.
Ready to apply?
Review the opportunity on the official website before applying.
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