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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 14 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 14 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
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Center for Drug Evaluation and Research (CDER) | Southern Md Facility, Maryland | United States | about 14 hours ago
, transport phenomena, material behavior, and product performance. Hybrid modeling approaches that combine mechanistic knowledge with machine learning. Model calibration, validation, uncertainty quantification
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 months ago
against simulation outputs. Run targeted simulations to test specific mechanisms. Month 5 — Validation, uncertainty quantification, and extended simulations Validate models across wider parameter space
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Source and conduct new experiments at these facilities. Collaborate with AI/ML researchers to incorporate forward models into inverse modeling, uncertainty quantification, autonomous analysis, and
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graph construction and mining, CDE development for data harmonization. Regulatory science and explainable AI, verification, validation, uncertainty quantification, and AI evaluation framework High
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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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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