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Field
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between Departments is important to the role which will be based in the Materials Innovation Factory. The post will involve leading an iterative programme of applied polymer chemistry and materials science
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, engineering, or a related field. Strong programming skills and experience in machine learning or statistical modelling are essential. Experience with healthcare data, algorithmic fairness, or deep learning
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the delivery of the 3-year EPSRC Funded ‘Digitally Enabled Circular Healthcare Innovation’ (DECHI) research programme. The aim of the programme is to investigate how advances in digital technologies and digital
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formats (DICOM, NIfTI) and image preprocessing tools (e.g., MONAI, SimpleITK). Excellent programming skills, demonstrated through available code or projects, with proficiency in Python and deep learning
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, MRtrix, ANTs, or equivalent, and programming or scripting in Python, R, or bash/zsh on Linux/HPC environments. Experience working with clinical research data, including neuroimaging and clinical data
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colleagues and make internal and external contacts to develop knowledge and understanding to form relationships for future research collaboration. To plan and manage own research activity, research resources
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students and over 500 Single Honours undergraduate students. We offer a 3-year BSc degree programme in Biological Sciences and a 4-year MBiol, while also contributing to Natural Sciences. We also run a PGT
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, artificial intelligence/machine learning, digital twins, and blockchain technology for operations and maintenance. This position is part of the Maritime Future Fuels Training Plan project, which aims
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programme of work jointly delivered between University of Liverpool and Mersey care NHS Foundation Trust (MCFT) until December 2027. It will have wide reaching benefits to the region whilst raising
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, artificial intelligence/machine learning, digital twins, and blockchain technology for operations and maintenance. This position is part of the Maritime Future Fuels Training Plan project, which aims