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MLPs to run classical MD simulations and characterise thermal transport. This PhD project will be based within the School of Engineering, University of Edinburgh. This PhD project will be supervised by
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an important role in the future energy system. However, the necessary adoption of electrolysers has been slow, limiting the accumulated relevant operational experience for confidence in the technology. Thus
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research activities. The applicant will have obtained or be close to obtaining a PhD in Mechanical Engineering or Materials Science. Appointment at Research Associate level is dependent on having a PhD
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are demonstrated through its extensive MSc and PhD research initiatives and its ongoing technology development programs in large-scale additive manufacturing. This project will be closely aligned with the ATI
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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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of Oxford. Unpaired electron spins are ubiquitous in materials and devices for optoelectronics and solar energy technology and play a crucial role in the fundamental photophysical processes at the basis
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-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those
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engineering or another relevant field applicable to the measurement technology development. For this position, we are unable to consider significantly different backgrounds, such as biology- and simulation
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This PhD opportunity at Cranfield University explores how next-generation AI models can be embedded within resource-constrained electronic systems to enable intelligent, real-time performance
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Engineering (PhD) Eligibility: UK Students Award value: Home fees and tax-free stipend £20,780 - See advert for details Project Title: Machine Learning and Optimisation-Based Intelligent Substation Design in