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Field
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cognitive, clinical, genetic, and proteomic data and manages them in the data repository and computer servers. Runs existing PET/MR brain image processing pipelines on the computer servers, produces
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at the intersection of theoretical chemistry and machine learning. Candidates with experience in method development and high-performance computing are especially encouraged to apply. A Ph.D. in chemistry, physics
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and stochastic analysis, numerical analysis, and scientific machine learning. Interest in interdisciplinary efforts is also valued. Commitment to and experience in teaching and mentoring student
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science, electrical and computer engineering, sociology, public policy, information science, communication, economics, political science, psychology, philosophy, and related technology disciplines. Selected candidates
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emerging spectrum (FR3, MMW, sub-THz/THz) Extreme massive MIMO communications Analog, digital and hybrid beamforming architectures Reconfigurable intelligent surfaces Machine learning for wireless
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of compressible flow regimes, including supersonic and hypersonic flows, as demonstrated by application materials. Familiarity with machine learning or data-driven modeling approaches in fluid dynamics, as
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machine learning, Computer vision, Swarms, Autonomous Robots, hardware security, and Embedded systems development is desired. The successful applicant will work on various projects on robotics and computer
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must possess substantial experience in artificial intelligence and machine learning methods, specifically in AI-driven materials discovery, machine learning applications for materials, or generative AI
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computer science venues, such as NLP conferences, machine learning conferences, and/or general interest journals Application Requirements Document requirements Curriculum Vitae - Your most recently updated C.V
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/child development policy, practice, and learning and development for analyze and interpret data, and preparing professional reports and academic journal articles. Certifications/Licenses Required