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should hold, or expect to achieve, a first-class or high upper second-class (2:1) honours degree (or equivalent) in Mechanical Engineering, Physics, or a closely related discipline. A relevant master’s
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quantum mechanical effects are typically too expensive for simulations of disordered systems like liquids. This PhD will develop and deploy the tools needed high-fidelity simulations: machine learned
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Institute, with close ties to the Department of Computer Science and Technology. Preferred skills/knowledge We are seeking a passionate and collaborative PhD student with a strong background in machine
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form of energy–e.g. electrical, magnetic, acoustic, or chemical–into mechanical motion. These swimmers are expanding the frontiers of micro-engineering and can be used in tasks such as chemical analysis
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matrix functions. These computational problems are central to many scientific and engineering applications, including quantum mechanics, materials science, and weather/climate modelling. Numerical methods
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degree or a master’s (or international equivalent) in a relevant mathematical sciences or engineering related discipline. Background knowledge in continuum mechanics, theory of partial differential
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. Entry requirements: Applicants will normally need to hold, or expect to gain, at least a 2:1 degree (or equivalent) in Mechanical Engineering, Electrical Engineering, Mechatronics and Control Engineering
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undertake a PhD in the NanoPhotonics Group (NP) led by Prof Jeremy J Baumberg, FRS (https://www.np.phy.cam.ac.uk/ ), as part of a prestigious European collaboration on building DNA origami for nanomachinery
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Are you fascinated by working at the intersection of physics and synthetic biology? This PhD project offers a unique opportunity to develop autonomous microswimmers, which are bioinspired structures
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-informed machine learning (PIML) with domain-specific engineering knowledge. By embedding physical laws and corrosion mechanisms into data-driven models, the research will produce more accurate