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inference attacks, to mitigate privacy leaks in MMFM. You will hold a PhD/DPhil (or be near completion) in a relevant discipline such as computer science, data science, statistics or mathematics; expertise in
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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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work will be exclusively in-silico analysis of human rhythmic behaviour, including sleep and chronotype, and cardiometabolic disease. We will use publicly available data and apply causal inference
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available data and apply causal inference methods, including Mendelian randomisation, to identify candidate mechanisms linking circadian misalignment and sleep disturbances with cardiometabolic disease
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. Armed with this information, the post holder will use cutting-edge paleoclimatic modelling that incorporates nutrient cycling and carbon chemistry (HadOCC) to infer the distribution of potential feeding
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) Experience of working with multiple stakeholders in complex systems. Experience in large scale simulations Experience in Bayesian methods Experience using CRAFTY agent based model Full details of the role and
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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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Atmospheric Science or highly related relevant field A proven track record in relevant atmospheric science modelling Knowledge of cloud microphysics and/or aerosol science Contact Name: Prof Maarten Ambaum
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plan to use these data to identify the virus and make inferences about potential human infection and transmission. This will involve analysis of viral evolution, simulation of potential scenarios and
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nanotechnology and electrical sensing investigations (with 2D nanostructures), with an established track record of success in high-quality original research. Applicants should have a solid grounding in different