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University Ithaca, New York Research Associate - Statistical Scientist The College of Agriculture and Life Sciences (CALS) is a pioneer of purpose-driven science and Cornell University’s second largest college
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RAP opportunity at National Institute of Standards and Technology NIST Coupling electronic structure methods, artificial intelligence, and data-driven approaches for next-generation quantum
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, organization, problem-solving, and critical thinking skills Proficiency with computer technology (i.e. word processing, e-mail, spreadsheets, databases) Strong written and oral communication skills and a
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 1 month ago
wording published by Decree-Law No. 123/2019 of August 28; Regulation of Research Grants of the Foundation for Science and Technology, I.P. in force (Regulation No. 950 / 2019 of 16 December https
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manufacturing systems including design/modeling/control of manufacturing systems, cyber-physical systems, applied AI, smart manufacturing and data-driven discovery/optimization, among others. Strong written and
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thin films. We aim to demonstrate their integration in superconducting qubit readout systems and validate KI‑TWPA technology for scalable quantum computing applications. This position is funded through
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of internationally visible, foundational research in AI-driven semantic structure extraction, automated reasoning-flow modeling, and adaptive content generation. The research focuses on methods for analyzing and
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-driven analytic tools to inform screening, prevention, and treatment strategies. Dr. Kang is a physician scientist and leader in cancer simulation modeling working with multiple collaborative teams across
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of the future, including: the use of faster processors being pushed by CubeSat technology; new requirements for cybers ecurity driven by actual threats; ever-higher data streams and volumes downlinked from
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Systems and Control division focusing on data-driven control methodologies. About the research project Model-based control is arguably the prime framework to perform certifiably-safe regulation of dynamical