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Computer Science, Artificial Intelligence, Software Engineering, or a related field. Strong programming proficiency in Python and/or C++. Demonstrable experience with machine learning frameworks (e.g., PyTorch
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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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computational statistics, machine learning, theoretical statistics, and probability as well as applied statistics fields, including statistical and population genetics, bioinformatics, econometrics, statistical
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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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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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design. Relevant research focus areas include, but are not limited to, hardware accelerators for machine learning (ML), Artificial Intelligence (AI), neuromorphic computing, and digital signal processing
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, computer vision, and predictive modelling, alongside excellent IT, analytical, interpersonal and communication skills in spoken and written English. You must be able to collect, process and present data with
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machine learning, Reliability Engineering & System Safety, Volume 264, Part A, December 2025, 111368 L. Deng, C. Shi, H. Li, M. Wan, F. Ren, Y. Hou, et al. Prediction of energy mass loss rate for biodiesel
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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