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have a 1st class degree (BEng or MEng/MSc) in electrical/mechanical engineering. Expertise in numerical tools (Ansys, JMAG, .etc) and programming are desirable. Experience in electrical machine
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the power of AI/ML and software-defined networking (SDN), and distributed learning methodologies, the research will focus on creating self-configuring, self-optimizing, and self-healing mechanisms for real
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health with the aim of creating healthier spaces and habits for them. This is a highly interdisciplinary project that combines computational modelling and behavioural science. The first part will be based
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electronics, embedded programming, signal processing, vibration measurement and analysis, maintenance engineering, and electro-mechanical engineering. Funding This is a self-funded PhD. Find out more about fees
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their research within the School of Engineering’s state-of-the-art facilities. The School of Engineering is well equipped with modern manufacturing systems, analytical tools and computing equipment. It also has
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degree (or equivalent) and/or a relevant postgraduate masters’ qualification (MSc) in materials science, engineering, mechanical Engineering, chemical Engineering, physics, or a related discipline. (E
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genomes. Dr Karam Teixeira's group uses Drosophila as a model to study transposable element biology, epigenetic regulation and inheritance, small RNA-based defence mechanisms, and germline biology in
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requirements A minimum of a 2:1 first degree in a relevant discipline/subject area (e.g. aerospace, automotive, mechanical, electrical, chemical, computing, and manufacturing) with a minimum 60% mark in
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to the development of multiscale computational models for simulating crack propagation and establishing reliable methods to predict the residual strength of composite structures. The simulations, performed in Ansys
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Discipline: Engineering & Technology, Fluid Dynamics, Mechanical Engineering, Other Engineering Research area and project description: Droplets are ubiquitous in nature, industry, and our everyday