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12.01.2026, Academic staff The Professorship of Machine Learning at the Department of Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%; initial contract 1.5
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tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in the form of graphs to analyze and predict food-effector systems. Key
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 19 hours ago
Degree Doctor of Philosophy (PhD) in Environmental Sciences Course location Cottbus Teaching language English Languages All courses are taught in English. The dissertation is written in English, and
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Degree PhD Course location Göttingen Teaching language English Languages English (100%) Programme duration 6 semesters Beginning Only for doctoral programmes: any time Application deadline Please
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 28 days ago
new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in
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. Signal processing, AI, and sensor systems: You possess strong expertise in signal processing, particularly using statistical methods and machine learning / artificial intelligence techniques. You also have
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, specifically methods that combine machine learning and optimization with physics-based simulation and/or physical constraints and translate these methods into impactful industrial applications. The position is
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take place monthly. A lecture series on theoretical and experimental neuroscience as well as machine learning is addressed primarily to doctoral students. Lectures are held by principal investigators
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principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion phenomena and link speciation with spectroscopic signatures. Formal requirements include a Master's degree in
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data