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use the data to train deep learning models of cancer. This allows us to identify systems-level mechanisms that can be used to uncover new biomarkers, drug targets, and paths to drug resistance. We
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use the data to train deep learning models of cancer. This allows us to identify systems-level mechanisms that can be used to uncover new biomarkers, drug targets, and paths to drug resistance. We
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and methodology for data synthesis, sparse representation learning, deep learning, fairness in generative models, as well as projects related to image capture, and image analysis The employment
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subjects are valued: signal processing, estimation theory, automatic control, dynamical systems, system identification, statistical machine learning, optimization, linear algebra, and deep learning. It is a
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networks. Deep learning for chemistry. The position requires very good knowledge of both spoken and written English. Scholarly proficiency must have been demonstrated through original research resulting in
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. This will leverage existing medical knowledge to enhance clinical data, will leverage deep graph representation learning such as Graph Neural Networks (GNNs) to showcase the capabilities of predictive
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multimodal image database integrating structural and biomechanical imaging of the pelvic floor muscles for improved assessment of birth injuries. Develop deep learning based image processing for pelvic floor
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subsequent scientific analysis and interpretation. The research group has a deep expertise and a strong track record with large-scale facility research at synchrotron and neutron facilities, such as MAX IV and
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cases. The postdoc position is linked to the research group Deep Data Mining, which focuses on fusing data science and artificial intelligence and developing AI trustworthiness (e.g., fairness, privacy