121 data "https:" "https:" "https:" "https:" "ASNR" positions at University of Southern Denmark
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technical team. Work structured and systematic and able to pass on knowledge. Effective oral and written communication skills (English). Basic computer skills and experience with data handling. We offer We
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Professor in AI-driven patient stratification and microbiome-based interventions (combined position)
data Explorative Network) , Odense University Hospital (OUH), respectively. The position is vacant as soon as possible, and the two 50% positions are considered as one entity (= full-time). Job
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we expect: Qualifications The candidate must have a PhD degree at the time of employment. We value candidates who combine strong experimental skills with an ability to integrate data-driven approaches
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collaboration with Odense University Hospital ensures excellent possibilities for translational research. Information about the department and contact details to professors leading relevant research groups can be
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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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Applicants should have strong experience in one or more of the following overlapping areas: Analog and mixed-signal IC design: amplifiers, references, data converters, sensor interfaces, and power management
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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
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degree or equivalent within Public Health, Medicine, Data Science or other fields related to Epidemiology or Environmental Medicine. The ideal candidate has prior experience in working with health data, is
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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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barriers and facilitators of prevention programs. The ideal candidate has a background in or experience with one or more of the following topics and areas: Survey methodology / Survey data analysis