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with artificial intelligence (machine learning/deep learning) Essential Application/interview Experience with classical image processing techniques (e.g. classification/segmentation/registration
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compliance with standards relevant to clinical trials (including GDPR, Good Clinical Practice (GCP), Computer Systems Validation, ISO 27001 and ISO 9001). This role contributes to the reliable delivery
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to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust, interpretable models from experimental and operational data
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in vivo and ex vivo respiratory disease research models with a keen interest and willingness to learn new research methods as part of a research team in the UK and USA. We offer an exciting and
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Role Overview As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have opportunities to tackle real-world, industry-relevant
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in vivo and ex vivo respiratory disease research models with a keen interest and willingness to learn new research methods as part of a research team in the UK and USA. We offer an exciting and
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in academia, industry and many related careers. Visit http://www.sheffield.ac.uk/sgs to learn more. Please apply for this project using this link: https://www.sheffield.ac.uk/postgraduate/phd/apply
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your communication abilities and experience the breadth of technologies that are used in academia, industry and many related careers. Visit http://www.sheffield.ac.uk/sgs to learn more. Please apply for
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and IT queries and you will also take on a variety of tasks to support the service using the Library and enquiry management systems. Our Learning Space Assistants are often the first source of help
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academic collaborators, you will contribute to cutting-edge research in artificial intelligence, multimodal learning, and machine translation. The role involves developing and fine-tuning large-scale