30 parallel-computing-numerical-methods-"Prof" Postdoctoral positions at University of London in United Kingdom
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developmental science. The successful candidate will contribute to a major research programme investigating how educational experiences shape mental health from childhood into adulthood. The role involves working
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About the Role We are seeking a researcher for a new programme focused on improving understanding of cancer risk and developing novel multicancer risk prediction models to support cancer prevention
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related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals
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to someone proactive and ambitious who has obtained their PhD and has begun an academic career in epidemiology, pharmacoepidemiology, or medical statistics, with a focus on causal inference methods applied
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Research Council’s (AHRC) Bridging Responsibilities AI Divides (BRAID) programme that will explore new technologies, new business models and new approaches to data provenance in pursuit of an equitable
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science. About You We are seeking a PDRA who will use advanced statistical and computational methods to analyse multi-omics datasets, such as genomics, proteomics, and metabolomics. You will develop
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Array Observatory. The Astronomy Group specifically focuses on applying advanced Artificial Intelligence (AI) methods to astrophysical research. As a result, we actively collaborate with experts in
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to deliver high quality publications. You will be familiar with the theoretical traditions that have informed Professor Cowards publications as well as broader qualitative methods – especially in the areas
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About the Role The School of Electronic Engineering and Computer Science at Queen Mary University of London is looking to recruit a Postdoctoral Research Assistant to support the projects led by Dr
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of computational and behavioural neuroscience with modelling and domestic chicks’ data. This position is funded by a Leverhulme Trust project entitled “Generalisation from limited experience: how to solve