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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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researchers and graduate students. Required Knowledge, Skills and Abilities AI/ML Expertise: Strong knowledge of advanced machine learning, deep learning, and AI techniques. Programming Skills: Proficiency in
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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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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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interest in methods development Experience in one or more of the following areas: algorithms development, transcriptome analysis, RNA modifications, statistics, machine learning, long read RNA-Sequencing
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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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ATAC sequencing, spatial transcriptomics, proteomics, whole-genome sequencing, functional screens, bioinformatics, and/or data algorithms including machine learning will be given preference. A successful
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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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in this position will conduct/lead applied as well as fundamental research in physics-informed Artificial Intelligence (AI) and Machine Learning (ML) methodologies enabling digital twin functionalities
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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