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responsibilities will include: Explore innovative methods for food process optimization including the use of AI and machine-learning Develop and execute methods for characterizing and linking the texture and
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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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IT and AV services that underpin technology-enhanced teaching, clinical simulation and hybrid learning environments. This is a pivotal role ensuring the reliable operation of technology-enabled
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further developing new and innovative academic practice and learning opportunities working in collaboration with our partners across England. You will work as part of the course team supporting
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Adversarial machine learning - Identification and prevention of cyber-physical attacks on infrastructure (S3.5-MAC-Champneys)
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background in Computing Science/Cyber Security/Computer Networks/Software Engineering or combination of both with relevant IOT/AI, IT, programming skills (C++, Python, Java or C#, OS etc.), CISCO Networking
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, and excellent learning and development opportunities. Informal Queries For an informal conversation about this role, please contact Dr Awinder Kaur, Head of Digital Technologies and Machine Intelligence
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feedback using machine learning and control engineering methods. The project will be hosted at the Bristol Robotics Laboratory (BRL), the UK’s largest academic centre for robotics research, with access
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particular emphasis on preparing analysis-ready datasets that support downstream statistical and machine learning workflows. What you would be doing You will contribute to data access workflows, database and
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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