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machine-learning surrogate models capable of delivering near-DFT (density functional theory) accuracy in just a few CPU seconds per structure. This approach will enable the high-throughput screening of tens
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, cross-disciplinary theory development, and improved STEM student engagement in entrepreneurship and innovation, towards a strengthened global talent pipeline for STEM-based start-ups. The successful
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Application deadline: All year round Research theme: Computational Chemistry, Material Science No. of positions: 1 Eligibility: UK students This 3.5-year project is fully funded by industry and home
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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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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the electricity generation mix continues to grow. Installed capacity in the UK in 2020 was 13.4 GW and is expected to increase to 40 GW by 2030. Accelerating the adoption of solar energy will present significant
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systems (eCPS). The focus in this research is on analysing and managing agent behaviour for enhanced sustainability and resilience. The student will be presented with a chance to shape actionable solutions
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the next generation of robots and its sensing solutions to perform tasks in challenging working environments. This project is related to the development of smart mechanisms and sensing to support the
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engineering or a relevant area. An MSc degree and/or experience and good knowledge in gas turbine theory, thermodynamics, Machine Learning, and computer programming will be an advantage. Funding Sponsored by
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pitting) and bending fatigue separately. Practically, there is a very high possibility that a gear is operated under an influence of both types of failures and hence reaches to its breakage well before