90 affective-computing-"https:" "https:" "https:" "UCL" Postdoctoral positions at Argonne in United States
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Python and either PyTorch or TensorFlow is required Experience using High-Performance Computers (HPCs) is preferred Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork
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(MSD), and Quantum Information Science (QIS) programs Disseminate results through high-impact publications and presentations at internal and external meetings Position Requirements Position Requirements
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samples and characterize dynamic behaviors. The candidate will be part of a highly collaborative team and actively interact with other groups, including optics, computation, and time-resolved research
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these areas. Ability to work independently on a day-to-day basis. Demonstrated interpersonal, oral, and written communication. Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and
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the domains of environmental, water, and energy system analysis. Prepares reports, papers, and presentations for conferences, workshops, and technical journals. Supports program development including
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journal articles. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. This position requires an on-site presence at the Argonne campus in Lemont, Illinois, five days
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ability to work effectively across divisions, laboratories, universities, and industry Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred Qualifications
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interpersonal, oral, and written communication skills. Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork. This position requires an on-site presence at the Argonne campus in
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of organization. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. This position requires an on-site presence at the Argonne campus in Lemont, Illinois. Preferred
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, distributions, and dynamics in metallic, oxide, and semiconducting systems. This project integrates high-throughput and in situ TEM experimentation with AI/ML-driven image analysis and computational modeling