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closely related field. Strong background in structural reliability, probabilistic modelling, stochastic dynamics, uncertainty quantification, or related areas. Experience in developing and implementing
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, efficiency, parameterisation, uncertainty quantification, and/or surrogate modelling. The research may involve the use of observational, experimental, or high-fidelity numerical data to train, validate, and
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skills. Experience in probabilistic analysis and uncertainty quantification within an engineering context, a track record of high-quality peer-reviewed research, and the ability to work independently and
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for Bayesian inference, inverse problems, uncertainty quantification, and scientific machine learning, with applications in environmental, scientific, and industrial imaging. The role/Te mahi We invite
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, race, gender, religion, marital status and family responsibilities, or disability. Key Responsibilities: The candidate will study theoretically forward and inverse uncertainty quantification problems
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: • Develop and benchmark multimodal AI / foundation-model approaches for spatiotemporal forecasting. • Build reproducible AI training and evaluation pipelines, as well as uncertainty quantification
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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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, recurrent memory, Bayesian modelling, uncertainty quantification and machine learning systems. Emphasis will be on methods that design and implement new architectures for (auto-regressive) sequence modelling
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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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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 10 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