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Field
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that transforms current AI for Science paradigms focusing on multidisciplinary applications in biomolecular modeling and design, leading to a step-change in Scientific Machine Learning (SciML). They will be
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energy systems, energy storage, buildings and district energy systems, smart-grid applications, process systems engineering, advanced control, and artificial intelligence/machine learning for energy
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engineering and clinical physiology. Projects may involve signal quality assessment, artifact detection, waveform segmentation, feature extraction, hemodynamic modeling, time-series analysis, machine learning
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looking for a postdoctoral researcher in the area of Generative AI. VITA research interests lie at the intersection of Computer Vision, Machine Learning (Deep Learning), and Human-Robot Interaction
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About the Opportunity Conduct research on machine learning, control theory, and synthetic biology. The work will combine tools from dynamical systems, control theory, and the theory of algorithms
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
the start date, a PhD in Data Science, Computer Science, Electrical Engineering, Electrical and Computer Engineering, or a closely related field. The applicant’s research should focus on machine learning
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(there is room for learning-on-the-job). A PhD in aerospace/mechanical engineering or applied physics. Demonstrated ability to conduct research in experimental fluid mechanics. Proven competence on flow
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Cornell University (5%). Qualifications: A Ph.D. in a relevant field (e.g. machine learning, ecology, biological sciences, computer science, statistics, engineering) Demonstrated quantitative skills
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cardiovascular care. Within the consortium, TU Delft contributes expertise in cardiac mechanics, soft tissue modeling, growth and remodeling, machine learning, and uncertainty-aware model personalization. As a
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),. Familiarity with data analytics, machine learning, digital twin knowledge, or Python programming language. Knowledge of additive manufacturing, process physics, thermodynamics, and/or metallurgy to interpret