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Field
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, sampling, inference, and machine learning. On one side, statistical approaches such as Bayesian inference play a critical role in identifying the parameters of PDEs, while on the other, newly emerging
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. • Experience with machine and deep learning modeling approaches and developing Bayesian models. • Multidisciplinary skills to bridge fields such as plant disease ecology, remote sensing data, and geospatial
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on the training strategies. In this project, we will investigate Bayesian methods to train deterministic SNNs (with deterministic activation functions) or probabilistic SNNs. Bayesian deep learning methods have
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processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary
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modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian hierarchical modeling using Integrated Nested Laplace Approximation (INLA). The work will contribute to ongoing
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received by November 1, 2025. Preferred skills: Demonstrated experience in modeling and applied statistics including machine learning, Bayesian statistics, multivariate statistics, model assisted estimation
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OtherProven ability to demonstrate creativity, innovation and team-working within work Proven ability to work without close supervision Desirable CriteriaExperience with Bayesian statistics Experience working
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 3 months ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine
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with statistical modeling (ideally Bayesian statistics) • Proficiency in Fortran, R, Python, Matlab, or ideally other common languages (e.g., C/C++) Strong computational skills Strong oral and written
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, data mining, Bayesian methods, and statistical learning About Working at the Crick Our values We are bold. We make space for creative, dynamic and imaginative ideas and approaches. We’re not afraid to do