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. Successful re-development for end-of-life composites could enable reuse in other structural applications. This PhD will investigate the development of hierarchical Bayesian algorithms to capture
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. Applicants with experience in Bayesian modeling, spatial statistics, mathematical modeling, data integration, uncertainty quantification and/or machine learning are encouraged to apply. The postdoc will also
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, 2022) and extended this to the triple equivalence between neural dynamics, Bayesian inference, and algorithmic computation (Commun Phys, 2025). -We validated it within in vitro neural networks (Nature
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Location: Ithaca, New York 14850, United States of America [map ] Subject Areas: Data Science / Statistics , Applied Mathematics , Artificial Intelligence , Bayesian Statistics , Big Data , Scientific
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intelligence, machine learning, big data and network analysis, computational and Bayesian methods, are encouraged to apply. Minimum Qualifications PhD in Statistics or closely related fields with documented
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experience would include most topics in modern statistics and topics like Bayesian Machine Learning and Simulation Based Inference (a past research focus on neural network architectures is not a prerequisite
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | about 2 months ago
dynamics in health and pathology; (2) in silico models, including Bayesian models, neural mass models and spiking neural networks; (3) in vitro neuronal network measurements. Our aim is to innovate in
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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during
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of the Biomaterials and Tissues of the Future. https://cordis.europa.eu/project/id/101226431 This network has 8 host institutions hiring doctoral candidates: Uppsala University, Universitat Politecnica de Catalunya
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. Desirable Expertise in computational fluid mechanics, broadly construed. Expertise in Bayesian methodology for optimization and experiment design. Experience with equivariant neural networks. Track record