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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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unique atmosphere where there is expertise to dig deep into computational modelling, while remaining connected to the experimental side. This interdisciplinary atmosphere has been a main catalyst for many
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image sequences. As a benchmark, end-to-end deep learning models will be developed using raw image data. In parallel, shallow learning models (e.g., Gaussian processes) will be explored based on insights
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extrapolating loads along the substructure, for any type of turbine, given specific geometric, inflow and sea-state information. Furthermore, such a machine learning surrogate can speed-up both design and
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for anomaly Detection and diagnostics: Leveraging state-of-the-art machine learning and deep learning models for automated fault detection, classification, and time-till-failure prediction. This will involve
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or altered behavioral responses in ASD models How these changes reflect underlying shifts in cortical circuit function You will work within the Bonin and Farrow Labs, which brings deep expertise in
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or altered behavioral responses in ASD models How these changes reflect underlying shifts in cortical circuit function You will work within the Bonin and Farrow Labs, which brings deep expertise in
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. The core objective is to develop advanced 3-D modelling and optimisation methodologies for magnetic components that enable accurate leakage inductance prediction and improved overall performance. Traditional
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of a doctorate, contains: Are you eager to improve pandemic preparedness through data-driven research? Do you want to work on real-world implementation of AI and epidemiological models in healthcare
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(Artificial Intelligence and Epidemic Modeling to Prepare Hospitals for the Next Respiratory Pathogen with Pandemic Potential). Project Overview The COVID-19 pandemic exposed critical gaps in our ability