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field. Preference will be given to candidates with: Previous experience in machine learning-related aspects of computational neuroscience, specifically with approximate Bayesian inference, and function
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of these approaches, and how to generate robust inferences. We would expect you to be highly skilled at using programming languages such as R or Python. It is important for you to have a track record of implementing
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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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An opportunity has arisen for a talented researcher with an interest in genetic epidemiology and/or causal inference to join Dr Stephen Burgess's research group based at the MRC Biostatistics Unit
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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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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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imaging data - Developing new methods for inference of copy number alterations from single-cell DNA sequencing data - Analysing patterns of single-cell copy number variation to identify mechanistic
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classification and the modelling of neuron and circuit function. Data sources will be neuronal morphologies, connectivity and computationally inferred or manually annotated metadata and published experimental data
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