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of research this position is engaged in: The Bowen lab leverages wide-scale neural recordings, predictive modeling, and continuous glucose monitoring with the goal of building foundational integrated (“multi
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, health and environmental stimuli jointly determine how animals function, adapt and contribute to ecosystems. PhD: Development of AI Models for prediction of resilience and susceptibility infectious
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and simulation techniques, prior distributions and posterior predictive checks, model comparison, programming in R (python/Matlab), implementations using R-packages rstan/JAGS and brms/STAN
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polygenic risk scores, rare variant burden scores, and integrative prediction models. Evaluate model performance and clinical utility. Identify therapeutic targets and causal risk factors for cardiovascular
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industry. The successful candidate will work in the established collaboration between DSB and ICGI to develop multimodal deep learning models for predicting prostate cancer aggressiveness. Specifically
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they change through time. To translate eBird observations into robust data products we create custom modeling workflows designed to fill spatiotemporal gaps based on remote sensing data while controlling
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be defined at two levels: SAACD Component: This is a UAV made up of hardware and software sub-systems, capable of observing, predicting, deciding and reconfiguring itself to fulfil its mission (e.g
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at Blindern, Oslo. Ocean waves follow complex patterns influenced by wind, currents, and the shape of the seafloor. Predicting extreme events like freak waves (unexpectedly large waves), frequency downshift (a
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to the lack of generation inertia worsening power system stability. Control of such a complex system relies on detailed understanding and real-time modelling of the nonlinear dynamics resulting from
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; machine learning methods (i.e. supervised and unsupervised learning, deep learning, reinforcement learning, etc.); artificial intelligence methods (e.g., predictive modeling, natural language processing