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Application Deadline 23 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 related to staff
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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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their relationship with treatment responses and disease activity. This is an excellent opportunity to contribute to a collaborative research program dedicated to improving our understanding of ALS and informing future
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of genome organisation and metabolic control—with the bold vision of building synthetic life. In this role, you will develop and apply computational methods to analyse single-cell modalities, focusing on gene
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associate with expertise in data science to join the King’s BHF Centre of Research Excellence and contribute to a growing cardio-immunology research programme. Inflammation is increasingly recognised as a key
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). Outstanding organisational and record-keeping skills and a capacity to work independently, once appropriate training and guidance is provided. Excellent academic writing, oral presentation skills and computer
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work closely with Prof. Hanna Kienzler, Prof. Stephani Hatch, and Dr Rebecca Rhead as part of the Centre’s ‘Marginalised Communities’ programme. This involves partnering with Black and racially
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of York. Based within the Addictions Department at the Institute of Psychiatry, Psychology & Neuroscience (IoPPN), the post-holder will support a five-year programme of research across three priority areas
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within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical questions and is aimed at novelty, understanding of physiology and
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& wider impact work). Secondly, you will do qualitative research with Prof. Ben Geiger and Prof. Karen Glaser as part of CSMH’s programme on ‘Work, Welfare Reform and Mental Health’. In particular, you will