236 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Denmark
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considerations. Experience working with machine learning methods for control, perception, or decision-making in physical systems is an advantage. Knowledge of or a passion for sustainable computing
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Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We are developing
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of fracture can directly inspire the design of new architected and disordered materials with enhanced toughness and reliability. Your profile Applicants should hold a PhD in mechanics of materials, physics
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Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer
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bachelor’s, master’s and PhD degree programs within animal science and veterinary medicine. We offer a lively, engaged and innovative learning and study environment, which is closely integrated in the research
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experimental techniques. As a formal qualification, you must hold a PhD degree (or equivalent). You must have: Documented expertise in either computational protein design or wet lab techniques, with a
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or related fields. Preferably strong interest and foundation in immunology, immune cell assays, or translational research. Preferably experience with computer-guided protein engineering. Experience in human
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include: Skills in mathematical modelling and machine learning of relevant physical glacier processes (ice sheet and mountain glaciers), with proficiency in MATLAB/Python/Fortran, and related software tools
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Engineering. Close collaboration with our neighboring Departments (Biosciences, Food, Agroecology, Chemistry, Mechanical Engineering, Electrical & Computer Engineering, Molecular Biology & Genetics and iNano
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be employed by the Department of Biology at Aarhus University and work in the Section for Microbiology at this department. The section employs 12 permanent scientific staff and ~20 PhD students and