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This 3.5-year PhD is fully funded; the tuition fees are paid and you will receive an annual tax free stipend set at the UKRI rare (£20,780 for 2025/26). The funding is linked to UK Research and
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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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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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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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) 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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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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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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, 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