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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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Surveillance group. This role supports the Agency’s mission to protect public health by applying advanced data science and machine learning techniques to enhance vigilance and safety monitoring of medicines
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for the challenging tasks facing biomedicine and public health. We are seeking a highly motivated Research Associate in Biomedical Machine Learning, who is passionate about advancing machine learning methodologies
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machine learning and hardware assurance. The project’s interdisciplinary nature provides a strong foundation for careers in applied research, secure electronics, or further academic study, with real impact
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role in the development of testing rig setups and the modification/re-purposing of existing rigs and equipment. Common activities include machining, cutting, water-jetting, welding, assembling and
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rig setups and the modification/re-purposing of existing rigs and equipment. Common activities include machining, cutting, water-jetting, welding, assembling and working with cabling and hydraulics
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to continuous improvement. Contribute to the development of appropriate programmes, modules and lectures in accordance with academic and military learning objectives. Participate fully in assessment
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practice and a commitment to continuous improvement. • Contribute to the development of appropriate programmes, modules and lectures in accordance with academic and military learning objectives
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an exciting opportunity to a graduate with the relevant skills and knowledge to develop and implement machine learning techniques to derive new-to-sector advanced insights from Aids to Navigation maintenance
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Design of a Fault Detection System for AI-Assisted Adversarial Attacks on Industrial Control Systems
AI-assisted adversarial attacks. You will work on topics such as cybersecurity, intrusion detection, adversarial machine learning, industrial automation, digital twin technology, and reinforcement