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(ED&I) and alternative routes into research. Desirable skills include experience in modelling and simulation (MATLAB/Simulink, Python), energy systems, and intelligent control methods. Applicants should
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such as Matlab and/or Python. How to apply Please send an email with subject “PhD studentship: Numerical simulation of boiling flows for high heat flux fusion components” to Dr Mirco Magnini, mirco.magnini
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programming skills in Python/MATLAB, and an interest in digital twin technologies, cybersecurity and machine learning. Entry Requirements Acceptable first degree: Computer Science or related disciplines
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and technology. Desirable (not essential): Research experience in sports medicine and technology (e.g. publication or final year project). Programming skills in MATLAB or Python. Application Procedure
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techniques (geomorphic mapping, TruPulse, DGPS/drone surveys), engineering geology methodologies (slope stability, rock strength assessment), coding (python/matlab data analysis and modelling) and transferable
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equivalent in a related discipline. This project would suit someone with: Experience with programming (Python, MATLAB), background in aerospace, computer science, robotics, or electrical engineering graduates
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programming (e.g., Python, MATLAB). Energy system modelling expertise with experience in academic research Preferred Skills: Educational background in Electrical Engineering, Computer Science, Renewable Energy
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environment Proven ability to use a scientific programming language such as python or MATLAB for signal processing A desire to improve therapies available to patients with neurological conditions Excellent
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systems have not been studied extensively. This PhD will model loads, generation, and the electrical network in Matlab/SIMULINK from an electrical network perspective and undertake network studies to assess
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configurations will be used to develop models in Matlab/SIMULINK of bipole converters with neutral current regulation. The research will then consider the potential interactions that may occur when all