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Field
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research which combined efficient optimization and sequential reliability assessment. The project is funded through an EPSRC call to accelerate research outcomes to achieve a prosperous net-zero and is
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i.e. turning towards in-line production and quality control will help save natural resources as well as reduce waste material and energy consumption. Formulation and test methods using mathematical
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evaluation frameworks and/or the development of energy system optimization models. The research is applied and closely linked to industrial interests and needs. About the research Our research aims to provide
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through advanced modelling and simulation. A key objective is to validate and optimize poroelastic finite element models of brain tissue, making them more accurate and clinically relevant. Additionally
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investigate the optimal methods for combining multi-satellite InSAR with a network of Kurloo GNSS devices to provide robust 3D ground motion monitoring from space. The potential benefits may include
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interdisciplinary training for PhD students. The programme supports you in pursuing innovative PhD projects with a strong application-oriented focus, ranging from mathematics, computer science, bio/life sciences
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optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in
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departmental duties, up to a maximum of 20% of full-time. Your qualifications You have a Master’s degree in electrical engineering, engineering physics, computer science, applied mathematics or have completed
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, engineering physics, computer science, applied mathematics or have completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses within the topics mentioned above
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extensive expertise in technology and systems for sustainable production of food and bioenergy, including optimal nutrition circuits and logistics systems. Within the field of methodologies, we have extensive