Research Fellow in AI for Autonomous Scientific Discovery and Scientific Machine Learning

The chair “Simply Complex Lab” focuses on understanding, controlling, and predicting emergent phenomena far from thermodynamic equilibrium. Its research is at the intersection of soft condensed matter, complexity, nonequilibrium, and nonlinear physics and interfaces with materials science, nanotechnology, and mechanobiology. The very nature of the lab’s research program is interdisciplinary; it is led by experiments but also strongly theory-guided.

As a research fellow (m/f/x), you will focus on developing scientific machine learning and AI-microscopy integration methods that can analyse experimental data streams and actively guide experiments through feedback., * Develop actor-critic and related reinforcement learning algorithms for experimental control.

• Design reward functions for pattern optimisation, exploration, and rare-event discovery.
• Investigate autonomous strategies for navigating high-dimensional experimental parameter spaces.
• Implement learning frameworks capable of operating on live experimental data streams.

Machine Learning and Pattern Recognition (Secondary Focus)

• Develop methods for crystal structure identification.
• Designed supervised and unsupervised learning approaches for pattern classification.
• Build models that identify precursors to structural transitions and emergent behaviour.
• Integrate multimodal imaging data into robust machine learning workflows.

Scientific Discovery and Analysis

• Analyse large-scale microscopy datasets.
• Develop quantitative measures of order, disorder, and structural evolution.
• Investigate mechanisms underlying non-equilibrium self-organisation.
• Publish findings in leading journals and conferences., The position is salaried and based on the collective agreement of the Länder (TV-L). If the personal and collective agreement requirements are met, the employee will receive pay grade E13 TV-L.

• 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.
• Experience with PyTorch and/or TensorFlow.
• Experience working with large datasets and scientific computing.
• Excellent scientific communication (written and oral) and collaboration skills.

In order to fill a fixed-term position in full-time (39.83 hours/week = 100%) at the earliest possible date, we are looking for a, Challenging and varied tasks with a high level of independence, Employment at one of the largest universities in Germany within the University Alliance Ruhr, Collaboration in a committed and appreciative team, Extensive training and professional development opportunities, A varied sports program with around 100 sport disciplines and the university’s own gym

• A unique opportunity to develop AI methods for real-world autonomous experimentation.
• Access to an established experimental platform and state-of-the-art imaging systems.
• Support from a Research Software Engineer and HiWi students.
• Strong opportunities for high-impact publications at the intersection of AI and physical sciences.
• Collaboration across machine learning, physics, and advanced imaging.

RUB sees itself as a university with an international presence. The campus languages are German and English. Competence in at least one of the two languages and the willingness to learn the other are a prerequisite. RUB provides corresponding free courses for employees.

German language courses are offered by the University Language Center (ZFA) in the field of German as a Foreign Language (DaF). https://www.daf.ruhr-uni-bochum.de/daf/mitarbeitende/index.html.en

The Staff Council has the right to participate in all selection interviews. At the request of a candidate (m/f/x), it will ensure its participation in the entire procedure. Please contact wpr@rub.de.

The Ruhr-Universität Bochum is one of Germany’s leading research universities, addressing the whole range of academic disciplines. A highly dynamic setting enables researchers and students to work across the traditional boundaries of academic subjects and faculties. To create knowledge networks within and beyond the university is Ruhr-Universität Bochum’s declared aim.