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Bachelors Honours degree (or equivalent) in an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject Area Medical imaging, biomedical
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computer vision method for medical image analysis principally in oncology or pathology, with a possible focus on generative AI, explainability, multi-modality. Candidate’s profile A good Bachelors degree
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engineering and nano/microfabrication Materials characterisation techniques Microbiology and cell culture Translational research and clinical collaboration Regulatory considerations for medical devices
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characterisation is advantageous How to apply This project is offered as part of the Centre for Doctoral Training in Engineering Solutions for Antimicrobial Resistance. Further details about the CDT and programme
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, materials, and biomedical engineering, offering training across fabrication, nanomechanical analysis, and computational biology. It contributes to more predictive and reproducible approaches in regenerative
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rather than the structured biofilms found in real-world environments. This project investigates how engineered surface topographies influence HGT dynamics, aiming to develop design principles for materials
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Training in Engineering Solutions for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT website Applications should be submitted by 12th January 2026.
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to overlook individuals whose bodies do not conform to conventional sizing or design standards, resulting in the exclusion of many disabled or medically restricted wearers from well-fitting and fashionable
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. What you should have: A 1st degree in physics or engineering. An interest in optics, some ability in computer programming A desire to learn new skills in complementary disciplines. You will work jointly
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biosensors that integrate platform sensing technology with tailored electroactive materials to enable precise and reliable detection of key TLS-related biomarkers using robust, user-friendly electrochemical