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Field
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date) in computer science, artificial intelligence, data science, applied mathematics, physics, electrical engineering, or a related field. You have a solid background in machine learning; experience
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development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning and neural networks Basic
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master’s (or international equivalent) in a relevant science, mathematics or engineering related discipline. Demonstrated excellence in fluid mechanics, machine learning, or both. Experience in programming
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discipline. Essential Good programming skills, preferably in Python/C#. Experience with machine learning, deep learning, or experimental AI evaluation. Interest in secure distributed AI, federated learning
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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | 1 day ago
Do you want to help transform human health through machine learning and life science approaches? Join AITHYRA in Vienna for a fully funded PhD at the intersection of machine learning, experimental
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that they meet the following screening criteria in order to be given further consideration as candidates: Education PhD in Analytical Chemistry, Biomedical Engineering, Chemical Engineering, or related fields. As
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opportunities? Do you have a background in electrical power engineering, operations research, or a related field, and are you interested in energy systems and markets? As a PhD candidate with us, you will work
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19 Aug 2026 Job Information Organisation/Company KU LEUVEN Research Field Engineering » Electrical engineering Engineering » Electronic engineering Engineering » Computer engineering Computer
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Job description We invite applications for a fully funded PhD position in the area of Scientific Machine Learning (SciML), which integrates data-driven machine learning techniques with established
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BMS constraints. Experience with system identification, uncertainty-aware modelling, large datasets, and machine learning. Evidence of research capability through a thesis, publications, conference