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This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites Research Groups at the Faculty of Engineering, which conduct cutting-edge research
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, machine learning, and information-theoretic approaches to achieve robust, non-intrusive security for the ever-expanding IoT landscape. Feature Engineering for Encrypted Traffic: It is crucial to identify
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energy; thereby minimising farming’s environmental impact. AI machine learning offers a new expedient method of developing control systems for tasks that would be difficult to manage using classical
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, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Strong background/skills on machine learning, mathematics, probabilistic
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for doctoral students. Overview This PhD project focuses on developing real-world deployable Machine Learning (ML) solutions integrated into Industrial Internet of Things (IoT) edge devices for condition
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behaviours? The proposed approach will focus on developing a multi-agent AI framework that integrates traditional penetration testing methodologies with machine learning techniques and advanced generative AI
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for their projects. AI transformation processes in projects and organizations Ethical considerations of using AI in project planning and delivery The use of project data analytics in AI-driven decision-making Machine
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formats available in conventional hardware are often too accurate for the needs of machine learning: they do not improve the quality of the trained model but may deteriorate it by causing overfitting
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structural alloys. The project will combine advanced phase-field fracture mechanics, continuum-scale chemo-thermo-mechanical modeling, and advanced machine learning techniques for enhanced prediction accuracy
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Open PhD position: Autonomous Bioactivity Searching Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 42-month funded PhD studentship will contribute to cutting