164 learning-"https:"-"https:"-"https:"-"https:"-"https:" "https:" positions in Sweden
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of the Medical Programme (semesters 1-5) using various forms of active learning. As a guideline, teaching, educational management and educational development work is expected to account for
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and Engineering, we are seeking a researcher with a strong interest in developing and applying machine‑learning methods for materials design, in particular steel design. The position is part of our
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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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continued good growth in our subject area. The department has education assignments in engineering programs and master's programs. More information is available on our website . ( https://kemi.uu.se
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, setting up the course webpage on the Canvas learning platform, delivering lectures and seminars, providing guidance and feedback on assignments, and conducting evaluations and examinations in
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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
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): Experience in AI algorithms, and machine learning Experience in Spatial proteomics Experience in splicing analysis The applicant must be able to integrate well into the international and multidisciplinary
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impact on sustainability and future technologies. For more information: https://saragovi.science/ Being a doctoral student As a doctoral student, you are both admitted as a student and employed at Lund
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experimentally. The research will be broadly situated in machine learning, including (but not limited to) algorithmic knowledge discovery, graph mining and social network analysis, optimization for machine
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encompassing perception, planning, and control. The PhD student will contribute to one or more of the following topics: theoretical foundations and geometric formalization, representation learning, robust