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experience in AI teaching We understand AI very broadly – and adequate experience would include most topics in modern statistics and topics like Bayesian Machine Learning and Simulation Based Inference (a past
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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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& Partnerships (NSF-TIP) directorate. More information on the project is available at: https://industriesofideas.ai/ . Term-limited: This is a term-limited position for two years, with the possibility of renewal
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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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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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learning models such as Bayesian optimization, neural networks, random forests. A high proficiency in spoken and written English. Excellent communication and interpersonal skills. You are expected to learn
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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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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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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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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | 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