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, particularly radionuclides, on a continental scale. The aim is to develop a new class of inverse Bayesian models, STE-EU-SCALE, combining innovative forward dispersion models, machine learning techniques, and
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understanding of statistics (e.g., hypothesis testing, Bayesian statistics) Good collaborative abilities, independence, and critical thinking. Preferred qualifications In-depth experience with LLM agents
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inverse problems. The team aims at developing Bayesian computational methods for such (ill-posed) inverse problems and aims both at increasing their validity and at reducing their computational cost. In
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simulators (https://doi.org/10.5194/egusphere-2025-2392). We have recently demonstrated its use in a Bayesian framework for evaluating South American methane emissions. In this role, you will continue the
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of Biostatistics and Population Health (BPH, https://medicine.osu.edu/departments/biomedical-informatics/divisions/division-of-biostatistics-and-population-health ) in the Department of Biomedical Informatics (BMI
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that can estimate atmospheric trace gas source-receptor relationships, or measurement “footprints”, orders of magnitude more quickly than traditional 3D simulators (https://doi.org/10.5194/egusphere-2025
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Design, Modelling and Simulations (MATHDES) group, and work under the supervision of: Matteo Croci. Google Scholar: https://scholar.google.com/citations?user=AmQKnwcAAAAJ&hl=en CV: https://croci.github.io
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, United States of America [map ] Subject Areas: Bayesian inference; inverse problems Appl Deadline: 2025/12/31 11:59PM (posted 2025/10/09, listed until 2026/04/09) Position Description: Apply Position Description
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induction, nearest neighbour classification, Bayesian learning, neural networks, association rules, and clustering are explored. The course also addresses approaches for handling unstructured data, including
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datasets. Proficiency with geometric morphometrics and image alignment. Proficiency in applying quantitative genetic methods to large datasets. Proficiency with large-scale animal models using Bayesian