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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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of the following backgrounds are particularly encouraged to apply: Machine learning and deep learning, particularly for time-series modeling Structural health monitoring (SHM), including fiber-optic sensor
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and sets up experiments in hybrid research environment. 2. Researches artificial intelligence/machine learning algorithms, database design, deep learning, big data, and cloud computing. 3. Publishes
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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
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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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involve developing methods for complex trait analysis, scalable Bayesian and deep learning approaches, or algorithms for inferring and analysing large-scale graph data structures. Experience in statistical
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, Mathematical Engineering, Mechanical Engineering or similar. Relevant skills: Strong background in machine learning/data science. Deep knowledge of neural network architectures (as a plus: PINNs, neural
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, Division of Applied Mathematical Science (Team Director; Eiryo Kawakami) (5) Medical Science Deep Learning Team , Division of Applied Mathematical Science (Team Director; Jun Seita) (6) Prediction
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record (EHR) as well as MyChart data, with the opportunity to work on applications of machine learning/deep learning/ Natural Language Processing in novel areas of healthcare. The position is open for a
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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