Research Fellow (m/f/x) in AI for Autonomous Scientific Discovery and Scientific Machine Learning

Aufgaben

Reinforcement Learning for Autonomous Experiments (Primary Focus)

• 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.

Profil

• 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.

Rahmenbedingungen

Fixed-term position in full-time (39.83 hours/week = 100%). Duration: fixed-term, 12 months (project duration). Start: at the earliest possible date.

Über "Simply Complex Lab"

Das "Simply Complex Lab" konzentriert sich auf das Verständnis, die Kontrolle und die Vorhersage emergenter Phänomene weit entfernt vom thermodynamischen Gleichgewicht. Seine Forschung bewegt sich an der Schnittstelle von weicher kondensierter Materie, Komplexität, Nichtgleichgewicht und nichtlinearer Physik und berührt Materialwissenschaften, Nanotechnologie und Mechanobiologie. Die Forschung des Labors ist von Natur aus interdisziplinär; sie wird von Experimenten geleitet, ist aber auch stark theoriegeleitet.

Bewerbung

Apply by: 2026-11-03