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29th June 2026 Languages English English English The Department of Mechanical and Industrial Engineering has a vacancy for a PhD Candidate in Machine Learning Approaches to Conservation Condition
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Heidelberg Institute for Theoretical Studies (HITS gGmbH) | Heidelberg, Baden W rttemberg | Germany | about 1 hour ago
) in Geometric Deep Learning to join the Machine Learning and Artificial Intelligence (MLI) g roup to perform research in geometric deep learning for materials science. This research is part of
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The continuous release of Earth Observation (satellite) data and the emergence of Machine Learning methods open up new possibilities for understanding forests. These large datasets provide
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applied mathematical modelling machine learning multi-fidelity modelling numerical methods. Demonstrated programming ability (MATLAB/Python/C++) and enthusiasm to learn PyTorch. Previous experience in one
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decision-making. Working with real-world data from Alliander, you will publish at leading machine learning venues while building tools with tangible impact on the Dutch energy sector. The Dutch energy
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preferably quantum machine learning. Strong interest in sustainability, education, and youth development. Key Responsibilities: Apply reinforcement learning to optimise resource allocation in hydroponic
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PhD Scholarship Opportunity - Processing intelligence for green metals using in situ X-ray characterisation and machine learning Job No.: 693787 Location: Clayton campus Employment Type: Full-time
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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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-based information. Where to apply Website https://www.academictransfer.com/en/jobs/364071/phd-positions-in-structural-hea… Requirements Specific Requirements You are a highly motivated researcher who is
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Mechanisms for Vehicular Networks Summary of the Scholarship Objectives: The scholarship aims to design, implement, and evaluate optimization solutions for vehicular networks based on Reinforcement Learning