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UiO/Anders Lien 8th July 2025 Languages English English English PhD Research Fellow in Machine learning Apply for this job See advertisement About the position Position as PhD Research Fellow in
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Job Category Faculty Non Bargaining Job Title Postdoctoral Fellow Department Rahmim Laboratory | Department of Radiology | Faculty of Medicine Posting End Date September 29, 2025 Note: Applications
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scientists, nuclear medicine physicians) to develop and implement innovative AI algorithms applied to medical images To lead effort on enabling translational and physician-in-the-loop AI solutions for medical
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– the Norwegian Centre for Knowledge-driven Machine Learning. We are looking for a motivated researcher, who has experience with both theoretical, methodological and applied research in change and anomaly detection
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, single-cell analysis, and machine/deep learning (preferred but not required). Strong programming and statistical skills (e.g., Python, Perl, R, Bash). Track record of first-author research papers. Strong
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on innovative design for legal and regulatory systems for AI and other complex global technologies; computational models of human normative systems; and working with machine learning researchers to build ML
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neuroimaging and fluid biomarkers, (b) systems biology analysis of pathways from multi-omics data using multi-layered network approaches, © machine learning for identification of genetic risk factors in ADRD, (d
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Research Fellow in Machine learning Apply for this job See advertisement About the position Position as PhD Research Fellow in Machine learning connected to the Scientific Computing and Machine Learning
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learning, computer vision, and natural language processing. You will have the opportunity to boost your career development and training, read more here . Post-docs receive highly competitive salaries and
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. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome profiling, or CAGE data