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CFD, thermofluids and machine learning. Experience in Python (or another language), machine learning frameworks, or CFD tools such as OpenFOAM is beneficial but not required. Applicants should hold (or
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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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Engineering Machine Learning) Operations Research and Optimization Methods CAD (Computer-Aided Design) Develop course materials and curricula aligned with international standards and industry intelligent
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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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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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within the climate change domain. The techniques are based on statistical and computational approaches, including machine learning algorithms. The project aims first to contribute to the prevention of fake
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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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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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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