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on accelerating high-impact advances in human performance by leveraging the rapidly advancing fields of bioengineering, neuroscience, artificial intelligence, genetics, molecular biology, imaging, and regenerative
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understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. Responsibilities: Conduct research on the mechanisms underlying
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chemistry or closely related field in physical chemistry or materials science completed within the last 5 years. Experience in applying artificial intelligence and machine learning tools for materials
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well as artificial intelligence and machine learning techniques (AI/ML) with emphasis on electronic properties (charge and spin) of a range of materials important to the DOE mission, including the materials classes
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 1 month ago
to artificial intelligence and machine learning, quantum information and sensing technologies, high-performance computing, communications technologies, semiconductors and microelectronics, biotechnology
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are required. Strong written and oral communication skills and ability to work both independently and collaboratively as part of a multidisciplinary research team are required. Knowledge of artificial
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involve the development of novel lattice QCD algorithms and high-performance computing (HPC) codes, and/or exploring applications of artificial intelligence (AI) to lattice simulations. The starting date is
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Practical and hands-on skills Who we are The Artificial Intelligence in Robotics (AiR) group, led by Dr. Andriy Sarabakha, focuses on the intersection of control systems, computational intelligence and
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activities to share knowledge and contribute to the academic mission of the institution, up to no more than 20% of working hours. Skills in utilizing artificial intelligence for materials design will be highly
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Correct), Bayesian statistics, systems theory and artificial intelligence are to be used as a basis in order to explore and implement a continuous calculation chain starting from observable and controllable