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research and unite our researchers around our Technology Innovation Challenges and Life Science Challenges. For more information on the Franklin’s Challenges click here . As a Research Associate in Chemical
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machine learning with spectral data to enable rapid, non-destructive detection of food adulteration and fraud. Machine learning combined with spectral data can play a vital role in combating food fraud by
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of Computing and Communications, such as the BSc programmes in Computer Science and Software Engineering, as well as the MSc programmes in Data Science and Cyber Security. Activities of the post include
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health data infrastructure and enable cutting-edge research nationally and internationally. They will work closely with teams across HDR UK and collaborate regularly with external partners—including
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the design of computer-based experiments. Candidates must have knowledge and experience of using specific psychology equipment and demonstrating this equipment to undergraduate and postgraduate students as
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. For further information, including details of how to apply, please see provided link using reference IAMXL. Alternatively email Belinda.beck@saxbam.com . The closing date for applications is noon on Wednesday
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on successful JEI operations. The role will require travel to the main University campus whilst hybrid working is also available. Further information about the role and the responsibilities is at the bottom
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communication, organisation and inter-personal skills are required as is a commitment to providing exceptional customer service. It is desirable that candidates have experience of working with large data sets
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however, we recommend that you assess your eligibility before applying for this position. For more information visit the Government Skilled Worker visa support page. However, applications are welcome from
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demonstrable experience of conducting Delphi-based consensus survey studies and literature reviews, alongside an understanding of confidentiality and data protection in research settings. Your background will