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data (PET, CT, Magnetic Resonance Imaging with Late Gadolinium Enhancement – MRI-LGE) and clinical variables. The approach encompasses unsupervised multimodal registration, three-dimensional deep
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(optimal) solutions—with subsymbolic approaches such as deep learning and reinforcement learning to reduce the complexity of knowledge acquisition and search for solutions. Therefore, this project is closely
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to diverse academic and industrial audiences. Proficiency in Python and deep learning frameworks such as PyTorch. Experience with Linux environments and GPU cluster management is essential. Competent in
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for candidates appointed as lecturers to teach online courses exclusively. For more information, please visit https://www.bu.edu/eng/academics/departments-and-divisions/electrical-and-computer-engineering/current
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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Description The Deep Learning laboratory in the Division of Science, New York University Abu Dhabi, seeks to recruit a Junior Research Scientist to work on Deep Reinforcement Learning (DRL
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comparing supervised and unsupervised methods (e.g., regularized regression, tree-based models, ensemble methods, clustering, dimensionality reduction) and deep learning approaches Developing and applying
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that shape our future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a
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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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structures and corresponding images) needed for training and validating deep learning (DL) models. Work closely with members of the ICMN nanostructures group or external collaborators. Communicate research