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Bayesian methods, deep learning, deep generative models, reinforcement learning, graph neural networks. Interviews are expected to happen in July 2025. Applicants are encouraged to guarantee that referees
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modern Bayesian modelling frameworks such as Stan, Turing.jl, and PyMC, including automatic differentiation frameworks, MCMC sampling algorithms, and iterative Bayesian modelling. Special attention will be
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research project results in a fluid mechanics context, i.e. a record of peer-reviewed journal papers in a relevant area. Proven track record, supported by high-impact publications as first author, of
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track record of successfully delivering aerothermal technology development projects, with specific skills relating to rotating turbine tests. We are looking for a diverse skillset, including good
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. The successful applicants will have a track record of science communication (e.g. presentations, outreach activities, teaching), and will have published in the field of plant biology. The successful applicants
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. Responsibilities range from monitoring instrument health and scientific quality of the data, tracking progress of the surveys, to preparing for community observing campaigns and data releases. The role's additional
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) in PhD/postdoc/job; has a good publishing track record, and has the ability to work in a team and with collaborators. The experience of system and component level testing in optical systems is an