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, plant transformation, plant breeding, computer vision assisted automated phenotyping, machine learning and AI. The role will require working with other institutional stakeholders to scope, design, equip
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productivity and output • Learn and be fully independent on the existing laboratory workflows, such as transcriptomics analytical pipelines running on Python such as Topometry • Manage own academic
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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, particularly MRI, medical physics or computational data analysis (Python/R/MATLAB, machine learning, or bioinformatics) is highly desirable. Interested candidates should send a CV to michael.chappell
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contributory pension scheme 38 days annual leave A comprehensive range of childcare services Family leave schemes Cycle and electric car loan schemes Employee Assistance Programme Membership to a variety of
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willingness to learn new skills and undertake further training and development aligned to the role, which may include working with laboratory animals Experience in working in a scientific laboratory, including
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An exciting opportunity has arisen for a Postdoctoral Research Assistant in the Department of Physics. Machine learning has made enormous progress during recent years, entering almost all spheres
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on shift A 6% employer contributory pension scheme Free car parking (if available) Life assurance x3 salary A health cash back scheme Cycle to work scheme Wellbeing programme including free yoga and bootcamp
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Foundation, explores how pathogenic bacteria survive and cause infection. Working at the interface of genetics, chemical biology and machine learning, the team develops small molecules to disrupt key bacterial
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of advanced big data analytics and machine learning methodologies to improve the planning, management, and operation of urban mobility systems. The successful candidate will undertake methodologically driven