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Field
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Responsibilities: Research and develop novel ML-based methodologies and algorithms in LLM-empowered Sub-Graph Learning for Large Graph Models. Working closely with other Postdoc/RA/PhD students to discuss the ideas
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successful candidate will primarily work on the development and enhancement of the OASIS model, contributing to new physical parameterisations, numerical algorithms, and/or performance optimisation. They will
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, Effects, and Criticality Analysis (FMECA), functional FMECA, advanced sensing techniques, sensor and operational data fusion, data analytics, and machine learning algorithms for condition monitoring, fault
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research team. Key research areas include: Development of low-carbon materials and tunable thermal energy storage materials integrated with smart sensors and advanced algorithms Creation of Digital Twins
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algorithms. The aim is to develop a high-performance intelligent motor control system that enables greater sustainability, safety and efficiency of the e-bike's energy system, in order to meet the requirements
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-based methods to achieve personalised and novel outputs. This position will have a particular focus on developing fundamental AI algorithms and methods that can be used in systems for real-time creative
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transport calculations and programming. Expertise in quantum transport and using machine learning algorithms for first principle calculations will be distinct advantage. You should be able to work
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-photonic computing architectures; Silicon-photonic network architectures Machine Learning Algorithms/Systems: Experience in design and use of ML algorithms; Experience in using ML for designing computing
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softwares and algorithms. Excellent publication track records. Good understanding of nonlinear and quantum optics. Strong knowledge of optical physics, while experimental experience would be preferred
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Electronic Engineering, Control Engineering, Computer Science or a very closely related topic: Strong understanding of power electronics principles Excellent knowledge on data-driven machine learning algorithm