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January 2026. This studentship is fully-funded by QR funds, the University of Hertfordshire and covers a three-year PhD. Supervisor Information Shoib Siddiqui, PhD Maria Braoudaki, PhD Mehrnoosh Ostovar
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Data-driven predictions of dynamical systems are used in many applications, ranging from the design of products and materials to weather and climate predictions. Mathematical concepts from geometry
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process and need help finding a supervisor, please contact swdtp-enquiries@bristol.ac.uk For further information about the SWDTP application process, pathways at each institution and details of strategic
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. You will work in an interdisciplinary environment spanning biomaterials formulation, nanoscale processing, and biological assessment. They will be responsible for designing and characterising new
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online for this vacancy and to view further information about the role, please click 'Apply' above. If you have any questions on the application process and/or the role, please email Mrs Alessandra Uomo
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This is a four-year (1+3 MRes/PhD) studentship funded through the Cambridge EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Unlocking Net Zero (FIBE3 CDT). Further
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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intrinsically, offering new solutions for photobatteries that operate more independently of fluctuating light, while offering new slutions for mobile or wearable (micro)electronics, and for light-information
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change accelerate, we urgently need smart, evidence-based tools to plan, manage, and protect our marine ecosystems. At the forefront of this innovation is machine learning. Its ability to process complex
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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