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Field
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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, particularly in areas such as machine learning theory, probability theory, optimization, and linear algebra. Experience with random matrix theory is a plus. Programming experience in Python. Familiarity with
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31.07.2026, Academic staff We are seeking a researcher in Scientific Machine Learning (SciML) to join the project "Data science at scale" at the Technical University of Munich, Germany. Ideal
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numerical simulations and machine learning in order to better understand and characterize active matter systems. A possible direction is to use physics-informed machine learning techniques to connect
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Learning Matters!. Task You will break with the current focus on the brain to uncover the physics of continual learning instead by investigating the emergence of learning bottom-up in life, reduced in
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and (2) develop learning rules that are both technology-feasible and well-suited for machine-learning workloads. The project will consist among others of the following tasks: Investigate and design
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prediction of machining results and improve process stability with fewer experimental iterations. The project integrates antibacterial coating strategies to predict coating efficacy and long-term surface
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sustain learning as technologies develop. Through case studies of AI policy, the project identifies capacity constraints and develops affordable feedback processes, open tools and exchanges between agencies
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, societal, and technological aspects of the environment shape this process. Working across disciplines such as psychology, neuroscience, computer science, economics, and sociology, we do research within a
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architectures for foundation models The work will combine methodological development with large-scale experiments, aiming for contributions at leading machine learning and computer vision venues such as NeurIPS