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into the sample of interest. Recently we have been using AI and machine learning to predict the distortion present and significantly speed up this correction process. This PhD project will take the latest in AI
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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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Adversarial machine learning - Identification and prevention of cyber-physical attacks on infrastructure (S3.5-MAC-Champneys)
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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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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