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Field
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P2S2 project aims at developing parton-shower algorithms with unprecedented (logarithmic) accuracy for jet substructure at the LHC. The project also has connections with analytic resummations and studies
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scientific data Major Duties/Responsibilities: Design and implement advanced AI architectures and workflows for imaging and spatiotemporal data. Develop efficient and scalable training algorithms
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration
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typologically diverse languages Creating self-supervised learning algorithms that can assess phonological development and speech complexity in children from birth through age 6, with applications to both typical
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Position Summary The postdoctoral fellow will develop artificial intelligence applications to support characterization of medical data with a focus on radiology image, radiology reports, and
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methodology will involve the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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development, smart grids and microgrids, focusing on the co-design and operation of an Energy Management System (EMS) at the state-of-the-art PROTEAS facility. In this role, the successful applicant will
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National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | about 4 hours ago
? Please email npp@orau.org Qualifications Background in engineering, computer science, or a similar field. Background in software tools such as Matlab, Python, and C/C++ Experience developing machine
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long ride, but a ride worth taking! Key Qualifications: Deep knowledge about how biological neurons have been trained before, and new ideas on how to train them Prior knowledge and experience in encoding