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will focus on three interconnected challenges: 1) Novel Inverse Reinforcement Learning (IRL) for Optimal Stopping Traditional IRL methods are not designed for noisy, trajectory-based optimal stopping
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the mentorship of leading experts in one of the following priority research areas: Research area 1: Intelligent Structural Optimization using Physics-Informed Reinforcement Learning Research area 2: AI-Enhanced
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compared to those produced via hand layup. Selecting the optimal combination of prepreg, reinforcement, and film to suit the complexity of a specific component prior to production trials is another critical
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, and high-performance computing for biotechnological applications. The project aims to develop computational tools for bioprocess optimization, combining modeling, simulation, and data-driven methods
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optimization. Create, edit, and publish web pages and landing pages within the StoryBlok CMS, maintaining quality and consistency across the site. Take a lead role in shaping and maintaining the site’s
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information about this on the UK Visas and Immigration Website . The University of Reading is seeking to recruit a PDRA in Holocene Vegetation Modelling to apply an eco-evolutionary optimality model of gross
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position aims to conduct holistic modelling and analysis of integrated energy systems to reach optimal system performance while incorporating various sustainable energy infrastructures. Potential research
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-efficiency trade-offs, using automated configuration to find Pareto-optimal designs under real deployment constraints. 2) Build the distributed learning loop. Develop the learning and update mechanisms
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of the ELITE system will be optimized, and by-products minimized. A range of material enhancements, electrochemical cell modifications, operational strategies will be explored for improved ELITE performance
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intelligence (AI) for application in medicine, with a primary focus on optimizing clinical trial design. The partnership will bring together the University of Oxford’s expertise in statistics, mathematics