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publications, and assisting in organizing and presenting at workshops and conferences; Take relevant courses and training; Participate in the PhD program activities and the intellectual life of the Institute
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will combine state-of-the-art computer vision, modeling and archived specimens to determine biotic and abiotic factors driving spatial variation in molt phenology. It will use museum genomics to recover
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. David Marlevi, Prof. Ulf Hedin, and Dr. Ljubica Matic to improve stroke risk prediction for patients with carotid atherosclerosis using a multidisciplinary combination of data-driven imaging
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vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid
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collaborations. The research group The position is in Ben Murrell’s group in MTC, based in the Biomedicum, in Karolinska’s Solna campus. The lab has worked across the experimental/computational interdisciplinary
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of these limitations and to test it in a real building. The final objective is to provide thorough documentation of the scalable MPC workflow and test case. The supervision team is Prof. Laurent
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of image analysis and machine learning with a minimum of 90 higher education credits. Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural
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document that you are particularly suitable for a PhD education You must meet the requirements for admission to the PhD programme in Medicine and Health Sciences Good oral and written presentation skills in
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relevant background within structural engineering, mechanical engineering, or computational engineering (or a closely related field). You must have a Master's degree in structural engineering, mechanical
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ecology with an emphasis on conservation biology. Your immediate leader will be the Head of Department. About the project The team of supervisors will consist of Ass. Prof. Kristine Bakke Westergaard (main