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interdisciplinary training in AI, modelling, and data analytics Contribute to real-world engineering applications Be part of the dynamic research community at the Zienkiewicz Institute for Modelling, Data and AI
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renewable energy, AI-driven engineering, and industrial research. Cranfield’s expertise in wind energy systems, predictive maintenance, and AI applications provides an ideal environment for cutting-edge
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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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Computational Fluid Dynamics (CFD) and Conjugate Heat Transfer (CHT) modelling, which captures both the fluid & solid domains, as required to develop this understanding for engine-representative geometries and
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). Robotics and Autonomous Systems (RAS) is an essential enabling technology for the Net Zero transition in the UK’s energy sector. However, significant technological and cultural barriers are limiting its
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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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resilient, long-term cost-benefit case for modernising and protecting our grid. The project will complement the ISS initiative and model data-driven grid optimisation services that ISSs can enable. Firstly
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power systems. You will join a large group of postgraduate students in the Faculty of Engineering, working on many aspects of solar energy and zero carbon technologies. The team of potential PhD
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power systems. You will join a large group of postgraduate students in the Faculty of Engineering, working on many aspects of solar energy and zero carbon technologies. The team of potential PhD
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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