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on persona-based social simulations for aiding policy design. Position Overview This research project will focus on building and evaluating novel LLM-based systems for performing computational social
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, including applications for external competitive funding and translation activities. Build a national reputation, based on a growing, focused body of work recognised for quality, excellence and impact and a
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learning and experimental validation. Key Responsibilities: Develop AI-guided experimentation methods integrating data, predictive modelling, uncertainty and validation. Build structured materials/process
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and equipment variability. Key Responsibilities: Develop machine-learning models for process prediction, state estimation, adaptive optimisation and closed-loop decision support. Build structured and
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excellence in all its dimensions: Searching for fundamental results and insights, developing novel computational solutions to a wide range of applications, building large-scale experimental systems and
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Department of Building Environment and Energy Engineering Postdoctoral Fellow (four posts) (Ref. 261006011) [Appointment period: each for twenty-four months] Duties The appointees will assist
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recognised for quality and impact. Build capability and mentor emerging researchers, supervising HDR students to successful, high‑quality completions. Strengthen partnerships and visibility by engaging with
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scholarship to research and appropriate external activities; make presentations at appropriate events, and write up research work for publication. Informal enquiries can be made to Professor Tony Wall, email
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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living
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bring new methods, partnerships or fields of enquiry. We are interested in excellent research, intellectual ambition and a clear sense of what you could build here over five years. Tell us what you want