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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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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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deep reinforcement learning, multi-agent systems, trustworthy AI, explainable/safe SI, neuro-symbolic/neuro-symbolic AI, and intelligent autonomous driving. Publish research findings in top-tier
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tomography research team with, for this position, a particular emphasis on computational imaging and deep learning. Tomography is an image reconstruction technique that leans strongly on large-scale numerical
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, preferably with applications to AI systems ● Design, analysis, and modeling of AI hardware such as deep neural network accelerators or neuromorphic computing ● Emerging AI/ML models and hardware
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Methodological competence: Strong programming skills, ideally including experience in deep learning and/or high-performance computing Passion for method development Very good English communication skills – both
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
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Control engineering (experience with nonlinear systems is a plus) Machine learning and deep learning in context of physical systems Programming skills are required, with Python experience preferred. A good
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are embedded in our Principles of Community , which reflect our passion for critical inquiry, debate, discovery and innovation, and our deep commitment to contributing to a better world. Every member of the UC