26 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"P"-"UCL"-"UCL" PhD positions at University of Southern Denmark
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Computer Science, fully funded by a major research project from the Novo Nordisk Foundation (NNF). The successful applicants would become part of the Data Science & Statistics section at the department
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portfolio of hypothesis-generating screening studies, a methodological portfolio where we identify signals of interest from large real-world register data to guide the conduct of further studies according
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collaboration with staff scientists in the team - publish scientific articles based on the data you generate - present research findings at research meetings and conferences Your profile: We seek candidates
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single‑photon detector (SNSPD). Additional responsibilities include developing efficient coupling of free‑space optics to optical fibers, conducting extended data‑taking runs with TES and SNSPD systems
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research environment and supervisor team with expertise in pharmacoepidemiology, pediatrics and child and adolescent psychiatry. We combine clinical perspectives and data sources with advanced population
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Intelligence, Data Science, Robotics or any other relevant field, prior to receiving admission into this PhD program, i.e., we accept applications from candidates expecting to finalize their studies within
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registration number) – in this case, the CPR number must be crossed out. Applicants are encouraged to read the information "How to apply" before applying. If you experience technical problems when uploading
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central role in SDU’s ambition to be a national and European hub for semiconductor and chip innovation. Further information For further information about the position, please contact Professor Farshad
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Information Further information about this position is available via email from Professor Ahmad Rafsanjani (ahra@mmmi.sdu.dk ). If you experience technical problems, please contact our email support
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. You will also spend 3 months at Georgia Tech/Emory University (USA), working on machine learning and data benchmarking. Work description The selected PhD student will be responsible for the full