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The primary objective of this project is to establish the evidence base on professional cycling road ‘racing’ trends and the critical tactical moments that determine how races are won. This evidence will inform future race strategy and live race tactics. Multiple factors influence the strategy...
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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders
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aligned subjects The Project start dates: October 2025 – Home students only January 2026 – Home and International students Supervisors: Aligned programme of study: PhD in Chemical Engineering Mode of study
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consider the multiple health and other impacts when deciding on appropriate action. This PhD studentship will conduct an assessment of the impacts of policies aimed at improving home energy efficiency
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the upper (water-repellent) and lower (fully wettable) leaf surfaces are crucial for self-righting. This fully funded 4-year PhD project takes a multidisciplinary approach to unravel the morphological
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) Applications are invited for a three-year PhD studentship. The studentship will start on1st Jan, 2026. Project Description Glioblastoma (GBM) is the most aggressive and treatment-resistant form of brain cancer
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and Technology (CST) at the University of Cambridge. The goal of this PhD programme is to launch one "deceptive by design" project that combines the perspectives of human-computer interaction (HCI) and
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Discipline: Engineering & Technology, Materials Science, Mechanical Engineering Qualification: Doctor of Philosophy in Engineering (PhD) This project is a collaborative research effort between
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Project details: Iron is crucial for athlete health and performance, yet iron deficiency is a common diagnosis in active populations. Iron deficiency can hinder haematological adaptations and reduce the body’s capacity for oxygen transport. Additionally, iron plays a vital role in energy...
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust