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beyond the Standard Model, including effective field theories and perturbative QCD, phenomenology at current and future colliders, as well as emerging areas in Artificial Intelligence, Machine Learning
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industrial conditions. Keywords: Artificial intelligence, autonomy, digital twin, edge computing, UAV systems. Objectives: Support the development of AI and machine learning algorithms for autonomous
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an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and agricultural datasets proficiency in R and/or
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago
Requirements REQUIRED: Ph.D. in Atmospheric science, Civil and Environmental engineering, mechanical engineering, or a related field; knowledge of and demonstrated skillset in physics-informed machine learning
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responses approximate human behavior. The project involves a collaboration between behavioral and computer scientists. The ideal candidate has some knowledge in both areas, and the specific behavioral domain
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innovative EU-funded project at the intersection of polymer chemistry, computational modelling, and machine learning. The primary role is to develop a complete in silico framework to accelerate the discovery
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to model them through computer simulations is highly valued. · Knowledge of classical molecular dynamics, including Machine Learning Interatomic Potentials. · Other research experience will be considered
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converter degradation/failure analysis. Brief Description of Duties: The Postdoctoral Associate will support the Principal Investigator, Dr. Fang Luo , in the Department of Electrical and Computer Engineering
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Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing
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Overview We are seeking a highly motivated postdoctoral researcher with expertise in artificial intelligence and machine learning (AI/ML) to join our interdisciplinary team at Duke University. The postdoc