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contribute primarily to developing and analysing Cortically-Embedded Recurrent Neural Networks (CERNNs) that simulate large-scale neural dynamics during cognitive tasks. These models integrate species-specific
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of extension by 1 year. This project involves searching new particles using the data from the ATLAS experiment at the Large Hadron Collider. The successful candidates will analyse data from the ATLAS experiment
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silicon technologies and/or participation in large-scale detector projects would be advantageous. For appointment at the higher advertised grade, applicants are expected to demonstrate an established
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investigating how dopaminergic and serotonergic systems interact in the primate frontal cortex to support learning and decision making. About the department The Department of Experimental Psychology is a large
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, using genomic and clinical data from large cohort studies on immune-mediated inflammatory diseases including psoriasis and rheumatoid arthritis. The PDRA will lead on research across all three objectives
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the interface of computational biology, molecular science and translational medicine, generating large multi-omic datasets that require robust, reproducible analysis to identify rare signals with high accuracy
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East London. REACH is delivered across three East London boroughs and brings together a large network of community partners alongside academic collaborators from the University of Cambridge. The Research
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within 3 months of applying. You will have proven skills and experience in quantitative analysis of large data sets for the evaluation of population health interventions and evidence synthesis. This is a
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computational methods for analysis and interpretation of large-scale biological datasets. Experiences in structural homology modelling is not essential but would be advantageous. The post holder will work in a
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teaching hospitals, the department works closely with clinicians and other research groups to ensure strong translational impact. Its mission is to revolutionise healthcare through data‑driven modelling