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based in the Department of Geography and responsible to Prof Tamsin Edwards (line manager and project lead) and Dr Alex Bradley (first supervisor), working with Dr David Watson (Department of Informatics
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(PDRA) with experience of statistical analysis and/or modelling, computer programming and preferably some meteorology/climate background, to join our growing team. You will work directly on the NERC
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About us The Social Computation and Representation Lab (SoCR Lab; www.socrlab.net) uses mathematical models and causal experimental designs to understand the fundamental rules behind human and AI
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) programme . The above full-time post is available immediately within the Department of Health and Community Sciences, University of Exeter Medical School. This is a fixed-term position until March 2026. About
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of agentic behaviour and publishing high-impact research. Candidates should possess a PhD (or be near completion) in PhD in Computer Science, AI, Security, or a related field. You will have a Strong background
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United Kingdom Application Deadline 16 Sep 2025 - 23:59 (Europe/London) Type of Contract Temporary Job Status Full-time Hours Per Week 37 Is the job funded through the EU Research Framework Programme? Horizon
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these bioinformatic experiments. Access to a high-performance computer will be provided. The candidate must be capable of generating complex molecular compound models in silico and using current molecular dynamic
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United Kingdom Application Deadline 14 Sep 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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Oxford’s Department of Orthopaedics (NDORMS) as well as collaborators in Bristol and Cardiff. You should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely