27 learning-"https:"-"https:"-"https:"-"https:"-"https:" "https:" PhD positions in Sweden
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PhD in Materials Science Norrkoping Reference number LiU-2026-03852 Join us in developing machine-learning accelerated simulation methods to understand and optimize interfaces in hybrid organic
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Academy, University of Gothenburg. General information about being a doctoral student at the University of Gothenburg can be found on the university's doctoral student pages. https://www.gu.se/en
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PhD student focusing on Machine Learning of Geometric Representations at ISY Linkoping Reference number LiU-2026-03853 We have the power of over 50,000 students and co-workers. Students who provide
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suitability for the learning and research tasks. Necessary qualifications: The applicant should by the date of admission to PhD studies have a second-cycle degree (e.g. MSc or equivalent) in
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professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/ Your work assignments Machine learning, and in particular deep learning, requires
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and apply AI and machine-learning methods to infer interaction networks from acoustic co-occurrence and activity patterns, and analyse their structural properties to link topology to response diversity
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. Doctoral studies end with a thesis and a doctoral degree. More about being a doctoral student at LTH on lth.se. https://www.lth.se/english/study-at-lth/phd-studies/ AEGIS — Adaptive Grid-Interactive
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Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including temporal data
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Computational Fluid Dynamics (CFD), fluid mechanics, and Artificial Intelligence (AI), with a particular focus on developing deep reinforcement learning methods for active flow control of hydraulic
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participating in the DARPA SUB-T challenge with the CoSTAR Team lead by NASA/JPL ( https://costar.jpl.nasa.gov/ ). Subject description Robotics and artificial intelligence aim to develop novel robotic