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model by integrating the newly developed approaches into the numerical program developed in the ‘OceanCoupling ’ project. We would like the successful applicant to start in the first quarter of 2026
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numerical model (RCM), at a regional scale, making use of simple, robust, and computationally efficient parameterizations to predict the evolution of a barrier island in response to sea-level rise
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to identify early phenotypic markers of efficacy and resistance and to discover predictive biomarkers for off-label therapies. To our knowledge, this will be the first large-scale application of Cell Painting
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methods to predict these needs individually at the time of stroke diagnosis. This PhD project aims to develop predictive models based on artificial intelligence (AI) that estimate a patient’s brain health
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leader will be Professor Jonathan Robert Whitlock (read more about the group at: https://www.whitlocklab.org/). About the project The successful candidate will perform large-scale neural recordings in
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system that predicts suicide risk across multiple time horizons from patient clinical journals, supporting earlier and more personalized interventions. The project will build upon a unique dataset of more
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in the context of a UiT grant that focuses on modeling spatio-temporal medical image analysis with a particular focus on learning from limited labelled data. The successful candidate will be a part of