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Research theme: Fluid Mechanics, Machine Learning, Ocean Waves, Ocean Environment, Renewable Energy, Nonlinear Systems How to apply: How many positions: 1 Funding will cover UK tuition fees and tax
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Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment
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of reinforcement learning or agent-based systems. LanguagesENGLISHLevelExcellent Research FieldComputer science » Computer systemsYears of Research Experience1 - 4 Additional Information Benefits • Full funding
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on Machine Learning and Psychophysiological Deception Detection. The studentship is part sponsored by GCHQ and funded for up to 3.5 years with fees and a stipend at the standard UKRI rate. The position is only
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, Psychology, or a related field, to be awarded before March 1st, 2026. Essential skills include an ability to code (e.g., Python, R) and interpret data, knowledge of machine learning and statistics, and a
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of the workflow. While the majority of the project is computer based, there is a small lab-based component in order to generate cell samples to be able to acquire the NMR data. Once proof of concept has been
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Subject area: Drug Discovery, Sustainability, Laboratory Automation, Microfluidics, Machine Learning Overview: This highly interdisciplinary 4-year funded PhD studentship will contribute to cutting
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projects in the Centre for AI and Robotics Research. Funded PhD projects Adaptive Systems Research Group Artificial Intelligence in Games Continual and Open-ended Reinforcement Learning Information and the
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
of next generation agentic AI systems. In this PhD programme, you will redefine how the world works, learns, and discovers, turning bold ideas into tools used by millions. You will then become one
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avenues may include linking plankton size patterns to krill dynamics, carbon export or nutritional quality, or developing tools for rapid ecosystem monitoring using machine-learning approaches