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Field
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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
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students. Selection criteria: • PhD/DPhil in a relevant field (or near completion). • Strong project management and communication skills. • Multidisciplinary and collaborative mindset
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets
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, such as Seurat and Scanpy. Experience with machine learning models, such as transformer and diffusion models. Strong written and oral communication skills. Modes of Work Positions that are eligible