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developing cutting-edge computer vision and deep learning aimed at optimising inspection and monitoring of infrastructure. Applying these advanced technologies to real-world infrastructure challenges through
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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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design of experiments methods, based on Bayesian Optimisation. In addition, the team at Cambridge has its own high-throughput and robotics facilities which we use as a testbed in developing new ML methods
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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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probability, likelihoods and Bayesian analysis. We are also seeking individuals with a strong interest in public health. Key Responsibilities: Develop models that integrate different data types (e.g., serology
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Cambridge Judge Business School (CJBS) leverages the power of academia for real-world impact to transform individuals, organisations and society. Since 1990, Cambridge Judge has forged a reputation
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on their own initiative and as part of a team, excellent interpersonal skills, familiarity with field science in human evolution, computer skills (spreadsheets and databases), excellent written and oral
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Cambridge Judge Business School (CJBS) leverages the power of academia for real world impact to transform individuals, organisations and society. Since 1990, Cambridge Judge has forged a reputation
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purchasing habits of 2000 households in the UK, this work has the potential for substantial impact in policy and the scientific literature. Work will include helping to develop a pipeline to estimate the
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to younger and older adults. In addition, the post-holder will help with organising lab administration. Previous experience with neuropsychological testing and strong computer skills (e.g., MS Office, R