286 learning-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions in Germany
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for Philosophy of Machine Learning for Science (m/w/d) to commence as soon as possible. The tenure-track position is embedded in the Cluster of Excellence “Machine Learning: New Perspectives for Science
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the vibrant Rhine-Main science region, the university can exploit its full potential and showcase its innovative power and dynamics. Its status as a comprehensive university fosters multidisciplinary learning
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competitive salary, aligned with German university pay scales (TV-L) and institutional regulations. Where to apply Website https://jobrxiv.org/job/ulm-university-27778-post-doc-reinforcement-learning-fo
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them using state-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work has provided insights into the origins and
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modeling, and scientific machine learning to enable efficient simulations of complex molecular processes across realistic time and length scales. https://doi.org/10.48550/arXiv.2604.24245 https://doi.org
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for teaching and conducting research in the field of organic chemistry and catalysis at the Department of Chemical Engineering. Your work will revolve around dedicated, practice-oriented teaching. You will teach
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Albert-Ludwigs Universität Freiburg | Freiburg im Breisgau, Baden W rttemberg | Germany | 22 days ago
2026 Where to apply Website https://uni-freiburg.de/stellenangebot/00005130/ Requirements Research FieldEngineering » Mechanical engineeringEducation LevelMaster Degree or equivalent Skills
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. To analyze and control thermoacoustic instabilities, we combine fluid mechanics, acoustics, and combustion with methods from control engineering, nonlinear dynamics, and data-driven modeling (machine learning
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the copan:LPJmL modelling framework (https://copanlpjml.pik-potsdam.de) to investigate the co-evolution of human land management decision-making and biophysical land dynamics. By explicitly coupling social
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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