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project entitled “A continually-learning framework for uncertainty quantification and translation of preclinical studies to human cardiovascular safety”. The aim of the project is to develop a statistical
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(PINNs) and surrogate modelling Time-series modelling and anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and
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anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and simulation Demonstrated Applied AI for Healthcare and
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 12 hours ago
data, bias analysis and minimization, performance metrics and uncertainty quantification, evaluation of continuously learning algorithms, and post-market monitoring. Learning Objectives: Under
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for optimization, history matching, decision making, uncertainty quantification, etc Conduct geothermal assessments, as this is a hot field (> 150 C) The candidate can also suggest other objectives / research
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interactive system design, serious games, or real-time human-machine interfaces. Experience with AI methods such as generative models, reinforcement learning, online/adaptive learning, or uncertainty
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-machine interfaces. Experience with AI methods such as generative models, reinforcement learning, online/adaptive learning, or uncertainty quantification. Research experience in rehabilitation engineering