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, engineering, mathematics or computational, have experience in the use of biomedical/clinical imaging data, have proven experience in programming (Python preferably) for medical imaging processing and machine
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details:Carly Bennallick. [email protected] Location: Denmark Hill Campus. Category: Research. About us: We seek a highly motivated and collaborative scientist to join our exciting new brain imaging
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imaging, electrocardiogram (ECG) and electronic health record (EHR) data to improve diagnosis, management and outcomes of heart failure. About You You will have either a PhD in the relevant subject (e.g. in
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to computer vision, particularly in the context of medical imaging. Some clinical experience is desirable, including interaction with patients. This is a full-time post, and you will be offered a fixed term
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. The candidate will join a vibrant research community at Southampton, the RFI, and the CLF, which includes physicists, chemists, computer scientists, and biologists all working to develop imaging techniques within
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, computer scientists, and biologists all working to develop imaging techniques within a supportive and diverse environment. The post will be based at the University of Southampton and employed by the School
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of epithelioids, DNA isolation and immunofluorescence staining and confocal imaging). -Actively participate in regular group meetings and other group activities. As a valued member of our team, you will have access
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tissue imaging data and clustering of rare cell populations (Bortolomeazzi Nature Comms 2022, Martens, Bioinformatics 2023). About You The successful candidate will be responsible to conduct statistical
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- on sensory data processing and neuromorphic hardware is one of the most promising routes for AI applications. The project goal is to demonstrate that interconnected nanoscale skyrmionic-based systems are a
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, funded by an internal MRC Impact Acceleration Award, we will use currently-collected patient imaging data to further develop computational pipelines to reconstruct personalised image-based computer models