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-driven techniques for the generation and exploration of complex, large-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel
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from plant genomics to phenomics with biological mechanisms embedded in deep neutral networks. GPTgp will allow task-specific training and transfer learning across reactions, pathways, biodesigns, and
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, teamwork, and discovery in finding new approaches for analyzing massive and complex data, collaborating with experts, and publishing significant results. Major Duties and Responsibilities: As a Computational
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for transmission or distribution grids, synchronous generators, large loads, transmission networks, etc. Develop simulation algorithms that enable large-scale simulations. Integrate (or co-simulate) grid component
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communication and scientific writing skills. Ability to work both independently and in large-scale, collaborative research teams. Experience with deep neural networks. Familiarity with NLP packages, pipelines
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developing them is a plus. Experience processing and leveraging large, complex remote sensing datasets is advantageous. Excellent written and oral communication skills. Motivated self-starter with the ability
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/Responsibilities: Designing and developing foundational statistical/AI/data-driven techniques for uncertainty quantification and visualization of complex, large-scale 2D/3D scientific data. Publishing research in
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environment. You will play an integral role in the development of PiFM and closely collaborate with other scientists across the network of NSRCs for synthesis, device fabrication, theoretical calculations, and
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processing software Knowledge of in situ neutron and x-ray scattering/tomography techniques for materials examination Experience with complex mechanical testing of materials Flexibility to adapt evolving
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, as necessary. Support research and development on alternative target designs, including alternative material matrices for pressed cermet pellets, complex geometries of pellets, novel encapsulation