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overseas. Training can be provided in computational fluid dynamics, machine learning, and nonlinear dynamics. These skills are highly valued across a wide range of industries. Recent data reveals that Fluid
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model predictive control (MPC) methods to enable large groups of buildings to dynamically form coalitions and provide flexible energy services. Your work will incorporate advanced robust MPC techniques
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Computer Science PhD Scholarships PhD scholarships are available in the Computer Science CDT, with some attached to specific projects. Eligibility: Home and international fee paying students
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Computation and Data Driven Design of Materials for Onboard Ammonia Cracking This exciting opportunity is based within the Advanced Materials Research Group at the Faculty of Engineering which
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accuracy is still limited. In contrast, computational fluid dynamics (CFD) models can capture the arc physics and molten pool dynamics, including arc energy transfer and liquid metal convection within
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on electromagnetic motors, pumps, or compressed air systems. However, motors are often bulky, heavy, and rigid, while fluid systems are typically tethered and inefficient. There is an urgent need for untethered soft
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spoken communication skills The following skills are desirable but not essential: Demonstration of undertaking research projects Ability to program Previous experimental experience in fluid dynamics
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project offers a unique opportunity to develop autonomous microswimmers, which are bioinspired structures at the micrometre scale that can propel themselves through fluids, mimicking natural swimming
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“lattice” version of space and time, similar to the finite difference approach in computational fluid dynamics. Using this Lattice QCD method, Centre Vortex fields will be analysed to understand particles
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Proteins are key biomarkers, indicative of normal biological or pathogenic processes and responses to intervention. Identification and quantification of such molecules in biological fluids is