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Field
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, non-linear contact interactions, and varying surface conditions. Reinforcement learning (RL) offers a promising approach to develop adaptive and robust control policies, but training on physical
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, organization, compression, analysis, and visualization of georeferenced or geometric data on large scales. We put emphasis on methods of distributed computing, machine learning, image and text analysis
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parameterization and shows limited adaptability to different vehicle types and operating conditions. The aim of the master's thesis is to investigate an end-to-end reinforcement learning approach for path tracking
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(Computer Science), in the research group “Computer Graphics and Virtual Reality” of Prof. Gabriel Zachmann, within the EU project SENSORAMA (Horizon Europe), we are seeking to fill the following position
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-based modeling and probabilistic machine learning, tackling problems that arise in molecular systems and heterogeneous materials. Eine Doktorandenstelle im Bereich physik-informiertes generatives
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04.03.2026, Academic staff The Professorship for Learning Analytics (LEAPS) at the TUM School of Social Sciences and Technology, Technical University of Munich, is seeking a Research Associate
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existing technologies, right through to the tested prototype. The Data-based Methods team at Fraunhofer ENAS develops real-world applications using AI, machine learning and computer vision. The main focus is
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Contribution to research on the integration of machine learning into quantum chemical methods and molecular simulation. A particular focus will be on the development, application, and evaluation
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working with empirical data; experience with statistical modelling, computational methods, machine learning, or causal inference is particularly welcome Programming skills (e.g., Python, R) - Experience in
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25.08.2026 Application deadline : 20.09.2026 The Cluster of Excellence "Machine Learning - New Perspectives for Science" together with the Tübingen AI Center at the University of Tübingen offers a