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recently completed a pilot study investigating the use of atmospheric pressure ionisation mass spectrometry coupled with machine learning for differentiating between brain tumours and normal tissue
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Supervisor: Professor Fernanda Duarte Start date: 1st October 2026 Applications are invited for a fully-funded DPhil studentship in Machine Learning Interatomic Potentials for Metal-Ligand
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: Earth Sciences, Bioscience, Interdisciplinary Life and Environmental Science, Inorganic Materials for Advanced Manufacturing, Chemical Synthesis for a Healthy Planet,Statistics and Statistical Machine
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and engineers. Key Responsibilities 1. AI Model Development & Testing Assist in developing machine learning and deep learning models for medical imaging analysis. Implement and fine-tune models using
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are committed to maintaining a safe and secure environment for our students, staff, and community by reinforcing our Safer Recruitment commitment. We're very proud to be a signatory of the Armed Forces Covenant
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are shortlisted. To learn more about the Institute for Health and Care Improvement and the Health and Care Research and Evaluation Service, please visit: yorksj.ac.uk/ihci For informal enquiries please contact
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to work with Bath colleagues engaged in energy research across the University, including advanced mathematics, statistics, and computer sciences Further information This role is offered on a full time (36.5
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. Qualifications Minimum: Master’s degree in engineering or a related discipline (e.g., Mechanical, Electrical, Computer, Energy, Materials, Mechatronics). Preferred: PhD’s degree in a relevant field. Prior
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Involvement and Engagement (PPIE) Lead. The project addresses the growing demands on glaucoma care by developing an AI-enabled triage platform that integrates retinal imaging, clinical data, and machine
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Involvement and Engagement (PPIE) Lead. The project addresses the growing demands on glaucoma care by developing an AI-enabled triage platform that integrates retinal imaging, clinical data, and machine