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theory proposed by Urbanczik & Senn (2014, Neuron) suggesting that plasticity is driven by prediction errors generated within neurons when the activity in dendrites does not match the activity in the soma
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). The post is funded by NIHR and is fixed-term for 24 months, with a possible extension. This project is about creating novel AI models to predict patient outcomes following acceptance or refusal of an offer
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practical knowledge of protein purification, ensemble biochemistry, and sample preparation to support your biophysical studies; • interact with biochemist and structural biology collaborators based
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acquiring a practical knowledge of protein purification, ensemble biochemistry, and sample preparation to support your biophysical studies; • come up with suggestions to expand the interdisciplinary
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analyses of viral proteins using AI-based prediction tools will also be integrated to support evolutionary inference. This work lies at the intersection of evolutionary biology, bioinformatics, and data
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(e.g. Homma et al., Nat. Comm 2023) to predict pathogen proteins that might be responsible for this manipulation. This project aims to elucidate the molecular mechanisms underpinning extracellular
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work on large-scale analysis of complex traits, including Bayesian machine learning and linear mixed model approaches for trait prediction and association in high-dimensional genomic datasets, as
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of plasma proteins with a wide range of health outcomes, identify novel and repurposing drug targets for diseases, and explore the underlying mechanisms of action and predict side-effects of specific protein
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statistical properties of AI procedures through the lens of topology and geometry, employing ideas from Stein’s method and conformal prediction. The post holder will also provide guidance to junior members
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Applications are invited for a Postdoctoral Research Assistant who will develop and implement predictive many-body theoretical methods to understand exciton scattering and dissociation dynamics in