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Field
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to complex challenges in this field. Essential and Desirable Criteria Solid foundation in computing principles, particularly computer graphics and machine learning. A 1st or 2.1 undergraduate (BEng, BSc, MEng
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in Vehicle Mobility in the Centre for Defence Engineering and Physical Science (CDE) at Cranfield University. Mr Kumar is the course director for MSc in Expeditionary Warfare Systems Engineering and
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Human Behaviour in projects targeting social good. Research at N/LAB focuses on the development and application of innovative computational methods using Big Data, Behavioural Science and Machine Learning
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multivariable statistical methods. Support for skills development is provided within the Horse Microbiome Research Group and the university’s Doctoral College . Delivery of this project in collaboration with
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. The start date for the PhD is the 15th September 2025. Eligibility Requirements: Applicants should have a minimum 2:1 degree in a health, physiology and exercise related subject. A taught MSc or Masters by
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degree or MSc degree (Distinction/Merit) in engineering, computer science, or other closely related fields. Experience with ROS and proficient programming language skills (C++, Python, MATLAB) would be
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to make decisions, solve problems, and learn. Moving from rule-based systems to agents with strategic flexibility. The range and complexity of tactics, techniques, and procedures that are supported, as
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. Experience in working with large data sets, knowledge of statistics, and some programming expertise is essential. The project is based in ECEHH, at the University of Exeter’s Penryn Campus in Cornwall, and may
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) publication at top tier ranked international conferences and journals in Computer Science. Person specification: MSc in Computer Science or AI (minimum of 2:1 degree or higher) UK home student status holder
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of further study at Masters level or equivalent (e.g., MSc research methods, health psychology, clinical psychology). Experience of conducting applied research. A working understanding of qualitative