73 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Technical University of Denmark in Denmark
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, you will contribute to research-based teaching and the supervision of student projects. Skills in mathematical modelling and machine learning of relevant physical glacier processes (ice sheet and
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collaborative effort, internally within experimental groups at DTU and externally with other leading universities. CatTheory has a strong infrastructure and computational facility. Responsibilities and
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Job Description These days, the inner workings of molecules and materials can be probed and modelled by advanced simulation tools on modern computer architectures. However, the routine applications
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estimation. Performing experiments to investigate synthetic samples, field samples, and samples from other projects in the program. This will require a well-established connection to other projects. Co
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of public-health professionals and PhD-students. The day-to-day work will be performed in close collaboration with other researchers in the Risk-benefit Research Group and within the project consortium
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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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. Research-based teaching in GNSS, geodesy, and surveying. The teaching must be conducted in Danish and English at bachelor’s (BSc) and master’s level (MSc) Co-supervision of BSc, MSc, and PhD projects Public
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, TESPy, or similar libraries. Strong programming skills in Python or MATLAB, including use of scientific libraries (e.g., NumPy, Pandas, Matplotlib, etc). Experience with machine learning (e.g., Scikit