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Field
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sustainable operation of future energy networks by combining our knowledge of energy systems with cutting-edge developments in machine learning, generative AI, and digital infrastructures. Your Job
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An exciting opportunity is available for a motivated and talented PhD candidate to develop a transformative technology for managing the UK’s nuclear graphite waste. Funded by the Nuclear
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companies. Hybrid & Data-Driven Modeling: Apply machine learning and hybrid physics-AI approaches to model industrial systems, accounting for physical constraints, sensor noise, and heterogeneous datasets
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measurement methods. These procedures target the assessment of steel properties for reuse in a new construction. Besides experimental work in the laboratory, machine learning will be employed to develop
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generative AI framework that utilizes machine learning predictions and quantum chemistry simulations to design stable, synthesizable, high-performance molecules. The framework will integrate multi-objective
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machine learning and/or operational research. You can understand literature in both disciplines. Excellent oral and written communication skills in English proven by a minimum score of 100 in TOEFL or IELTS
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University, the Communicable Diseases Agency Singapore (CDA), the National Environment Agency Singapore (NEA), the Machine Learning & Global Health Network (MLGH), and wider regional and global partners. Key
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intelligence, or related areas Experience with large language models, machine learning systems, software testing, program analysis, requirements analysis, or software quality assurance Strong programming skills
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embed user-defined conditions and structural constraints directly into machine learning routines to achieve high-resolution 3D interpolation of rock mass properties. These synthesized geotechnical fields
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» Maritime engineering Engineering » Computer engineering Computer science Architecture » Naval architecture Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 23 Aug