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Field
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have exceptional resources to facilitate research including access to administrative, research, and computer support staff. Required Qualifications* PhD degree or equivalent in epidemiology, gerontology
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transportation operations and network modelling, accessibility analysis, data analysis (statistics and/or machine learning methods), and spatial mapping. Because the work will involve multiple years of daily
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interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and functional neuroimaging data. Specific activities may include
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have been received within the last three years, 1 year of experience with machine learning, natural language processing, AI tools and frameworks, data integration, and/or explainable AI. Proficiency in
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and standing recognised by the University/profession as appropriate for the relevant discipline area (e.g., AI/Machine Learning, Bioinformatics). A proven track record of research and scholarly
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developing machine learning or data science approaches for patient stratification and genetic association analyses using cardiac magnetic resonance imaging in biobank populations. Successful applicants will
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The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently adopted in CLM-FATES at UiO. The aim is: to constrain
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machine learning techniques to enhance source detection sensitivity. Multimessenger modelling and interpretation of Galactic sources. This position offers the opportunity to work at the forefront
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data science, machine learning preferred. - Strong programming skills in R and/or Python required. We invite applications from qualified candidates who share our commitment to employment equity and
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The Computational Neuropsychology & Simulation (CNS ) Lab and Dr. Thomas D. Parsons, PhD invites applications for a postdoctoral fellow focused on research in social neuroscience, computer science