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statistical approaches. A fundamental understanding of Deep Neural Networks as applied to high-frequency time series datasets, including the ability to design and implement custom NN models in PyTorch, as
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materials property predictions. A deep understanding of materials properties and close connections in academia and industry enable the group to explore exciting research avenues. For more information about
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collaborative research environment which will provide the opportunity to perform cutting-edge research in deep learning and scientific computing. Deliver ORNL’s mission by aligning behaviors, priorities, and
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). Team player and great collaborator Strong interest in interdisciplinary work at the interface between dementia/ neurodegeneration, modeling, and machine learningPrior experience in deep learning, or/and
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areas of concerns to improve healthcare delivery to people with a learning disability and autistic people. We are contracted to deliver an annual report, regional reports and a number of deep dives as
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, operations, and culture of DOE. As a result, fellows will gain deep insight into the federal government's role in the creation and implementation of energy technology policies; apply their scientific, policy
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, computer science, bioinformatics, or other related disciplines is required. Strong interest, research background and experience in the methodology research in statistical genomics, machine/deep learning
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deep exploration of cancer precursors (precancers) to identify their molecular vulnerabilities and developing methods to intercept them. The alliance is led by Professor Sarah Blagden. You will be
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the aim of conducting deep exploration of cancer precursors (precancers) to identify their molecular vulnerabilities and developing methods to intercept them. The alliance is directed by Professor Sarah
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groups to study cancer biology using deep clinicogenomics data and cutting edge NGS diagnostics. The Opportunity: Opportunity to drive scientific, analytic, and technical innovation in a community of