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Field
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position is central to the project in that you are expected to collaborate with project members to develop algorithms and knowledge within AI, through the development of experiments and simulations, and
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postdoctoral researcher with a solid background in one or more of the following areas: game theory, optimization algorithms, and numerical methods. The successful candidate will develop efficient algorithms and
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demonstrations. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development. Responsibilities will include programming
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-driven research group working at the intersection of computational genomics, clinical artificial intelligence, and imaging genetics. This position offers an exciting opportunity to develop novel algorithms
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of science; (b) develop novel metrics and algorithms for AI explainability; and (c) create a tight feedback loop where (a) and (b) can iteratively refine each other. As a postdoc, you will be based in the Data
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materials science applications and algorithms for OLCF supercomputers Develop and apply advanced, compute-intensive materials simulation methodologies to study the fundamental properties of molten salt
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to the development of remote sensing algorithms, simulation and modeling capabilities, and artificial intelligence/machine learning (AI/ML) methods in support of the goals of the joint NASA/USGS Landsat mission
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basic and applied engineering research, workforce development and technology transition. Our collaborations with industry, academia and government provide cutting-edge solutions to global technical
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orchestration across different network components, as well as to the development of sensing algorithms and ISAC solutions. The postdoctoral researcher will also play an active role in coordinating research
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algorithms for fluid-structure interaction solvers What you will do Independently develop numerical methods and codes for fluid-structure interaction of wind-powered ships Carry out and analyse high-fidelity