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Field
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the data from HEP experiments is strongly required Programming expertise in Python and either PyTorch or TensorFlow is required Experience using High-Performance Computers (HPCs) is preferred Ability
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 5 hours ago
efforts at ARC are focused on understanding the structure-property behavior of ceramic materials in high thermal gradient environment with implications on manufacturing and system performance. Relevant
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Computing Applications group in CSD has an immediate opening for a Postdoctoral Research Associate to design, develop, and deploy machine-learning and high-performance computing workflows, algorithms, and
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Science, Computer Science, Applied Mathematics, Engineering and Physics. Additional Qualifications Expertise (or desire to work) in reduced order modeling, Causal inference and High Performance Computing
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development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high-performance computing for one or more of the following: (1) reconstruction
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opportunities for high-impact publications and grant development. Key Responsibilities Perform computational analysis of large-scale omics datasets, including proteomics, transcriptomics, and related modalities
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development, and High-Performance Computing (HPC). The postdoctoral researcher will have opportunities to engage in these areas in support of physics goals. Key responsibilities Lead and contribute to ATLAS
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a high-performing workforce through competitive compensation and career development including career pathing, coaching and skills development. Recognize and reward exceptional performance based
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independent and collaborative theoretical and computational research in quantum condensed matter physics Develop and implement high-performance scientific software, and simulation tools Publish research papers
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fluorination chemistries, and the development of high-performance materials through both top-down and bottom-up approaches. Candidates should be prepared to broaden their expertise across multiple material