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Field
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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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multidisciplinary team of scientists and engineers working at the forefront of hydrogen materials research. The project will focus on the optimisation of metal (complex) hydrides and the development of advanced solid
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, reinforcement learning, simulation, and counterfactual analysis. Multimodal NLP & Fusion, large language models (LLMs), cross-attention Fusion, vision-language transformers. Ontological engineering, knowledge
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angle measurement, electrochemical analysis, ion chromatography, ICP-OES/ICP-MS, etc. Experience or interest in AI-assisted analysis, data-driven materials development, computational modelling, and/or
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• Integrate diffusion models for visualisation and content generation • Support text-to-scene and voice-to-scene workflows • Implement in real-time engines (e.g., Unity/Unreal) 3. Platform & Workflow
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in vivo experiments using genetically engineered mouse models and disease models of muscular dystrophy. · Apply genomic and biochemical approaches to define transcriptional regulatory
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. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases, shaping the future of medicine
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interests, the work may include: Developing new models and methods for noise-driven wireless communication. Designing and evaluating low-power and low-complexity signaling schemes for future IoT and 6G
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Research Fellow (Software, AI & Autonomous Systems) Required Qualifications PhD in Robotics, Computer Science, Artificial Intelligence, Electrical, Computer Engineering, or related disciplines. Key
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, water use, and carbon emissions. You'll work side by side with talented scientists and engineers from national laboratories, universities, and industry, all collaborating to rethink how critical materials