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knowledge; (d) develop reliability-aware diagnostic methods involving uncertainty quantification, confidence calibration, conformal prediction, out-of-distribution detection, and unknown fault recognition
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-funded project entitled “A continually-learning framework for uncertainty quantification and translation of preclinical studies to human cardiovascular safety”. The central aim of the project is to develop
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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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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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), functional genomics, or comparative genomics at scale. Experience with model calibration, uncertainty quantification, or active learning. Experience with genome-scale metabolic modeling or pathway analysis
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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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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 8 hours ago
for small preclinical drug safety datasets, with attention to overfitting, uncertainty quantification, and regulatory defensibility across varying sample sizes and data variabilities. Develop applied
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 8 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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, spatial and temporal image data trustworthy AI for sensitive and distributed data, including privacy-preserving and federated learning, uncertainty quantification, and efficient real-time inference in
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with statistics, uncertainty quantification or uncertainty propagation is an advantage. Familiarity with other relevant programming languages is an advantage. Applicants must be able to work