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60203 E-Mail service.rh-recrutement@utc.fr Phone +33 (0)3 44 23 79 69 +33 (0)3 44 23 79 69 Fax sans objet STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options E-mail Pocket
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60203 E-Mail service.RH-recrutement@utc.fr Phone +33 (0)3 44 23 52 81 +33 (0)3 44 23 52 81 Fax sans objet STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options E-mail Pocket
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Lecturer (BIOS 691) Winter 2026 BIOS 691 Special Topics in Biostatistics (4 credits) Winter 2026 BIOS 691: The aim of this course is to provide researchers with an introduction to practical Bayesian methods
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 6 hours ago
hypotheses and computational analyses. Integrate genetic, molecular, and clinical features to identify mediators linking genotype to phenotype using mediation and causal inference frameworks (e.g., Bayesian
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these changes affect ecosystem functions. To extend these analyses to new types of data and questions, we develop state-of-the-art hierarchical Bayesian methodology. We also actively apply our research to more
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Research Engineer/Postdoctoral Position Decision and Bayesian Computation (DBC) – Epiméthée (EPI) Laboratory Institut Pasteur, Paris | 25 rue du Docteur Roux, 75015 Paris Position Overview We
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have recently demonstrated its use in a Bayesian framework for evaluating South American methane emissions. In this role, you will continue the development of GATES under two grants from the Natural
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-2392 ). We have recently demonstrated its use in a Bayesian framework for evaluating South American methane emissions. In this role, you will continue the development of GATES under two grants from
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differences. Then you will use diagnosed processes to build reduced-order models and constrain model parameters using historical instrumental and palaeo-proxy records via Bayesian methods. The successful
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comparing models with entirely different structures and parameter counts, whether comparing linear regression against mixture models or decision trees. MML is strictly Bayesian, requiring prior distributions