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: Advancements in biosensor technology are at the forefront of modern biomedical research, addressing the growing need for precise, real-time monitoring of biomolecules and overcoming critical challenges in
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Antimicrobial resistance (AMR) is one
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entry requirements: The student should have a background in geoscience, civil, chemical or mining engineering. They should have strong mathematical background - it would be advantageous if the have some
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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Engineering Solutions for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT webiste Applications should be submitted by 12th January 2026.
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developer and user meetings. You will acquire skills in programming (e.g. Python, FORTRAN, bash) development of quantum chemistry software and stand-alone tools a wide range of computational and quantum
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Deadline extended: 30 October 2025 (originally 29 September 2025) A fully funded 3-year PhD is available in the School of Computer Science and Engineering (SCSE), Bangor University. The project will
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between reliability, performance, computational efficiency, and adaptability under uncertainty. The candidate will be affiliated with CRADLE (Center for Robotic Autonomy in Demanding and Long-Lasting
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science, engineering, mathematics, or related subject) Proficiency in English (both oral and written) Essential to have strong foundations in computer systems through degree courses or equivalent work experience
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farms at relatively close proximity can be relevant when considering their annual energy production. This project will examine the uncertainty of various types of numerical models, from fast-computing