50 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" positions at University of Copenhagen
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(DInSAR). Minute surface uplift and subsidence signals will be automatically detected using machine-learning workflows, enabling systematic, user-independent identification of drainage events every 6–12
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data
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project, should contact Principal Investigator prof. Mikkel Bille, mbille@hum.ku.dk , phone +45 35329480 Link to the department’s website: https://saxoinstitute.ku.dk Introduction PhD studies consist
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supervision and training of research fellows and other staff. The successful applicant must also teach, supervise, prepare and participate in examinations, and fulfill other tasks requested by the Department
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of Health & Medical Sciences at the University of Copenhagen, commencing as soon as possible. Information on the department can be found at: https://bmi.ku.dk/english/about_department/ Information
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data management and machine learning is also preferred. An interest in energy system topics such as the green transition, sustainable energy systems, digital energetics etc. is preferred. Experience
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, please contact the principal supervisor Anne Sofie Hammer. General information about PhD study at the Faculty of Health and Medical Sciences is available at the Graduate School’s website: https
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for veterinary antimicrobial stewardship and infection prevention. Information on the department and the section can be found at: https://ivh.ku.dk/ . Our research The Assistant Professor will be part of the OHAR
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qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at https://employment.ku.dk
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working with large collaborative code projects Proficiency in applying AI-driven algorithms (e.g., neural networks, reinforcement learning) for the creation of surrogate models and the autonomous