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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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design and optimization of hydrogel coating systems. The innovation lies in establishing a closed-loop workflow specifically for interfacial mechanics, with multi-scale integration and physics-informed
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of these optimal protocols. Funding Notes This project is for self or externally funded students only. References https://www.quantumbespoke.com/ View DetailsEmail EnquiryApply Online
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are inherently highly complex. In this research project you will use state of art AI-based optimization algorithms to develop new functionality into industry-relevant digital design tools (CAD) to support
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. Optimal sensor placement, identified through adjoint-based sensitivity analysis to improve assimilation efficiency. By embedding physical laws into data assimilation, these methods bridge the gap between
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swapping—an approach not yet systematically studied. This dual functionality raises technical and operational challenges that the PhD will address across three areas: 1) System Sizing and Optimization
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of art AI-based optimization algorithms to develop new functionality into industry-relevant digital design tools (CAD) to support disassembly tasks in the energy sector (wind turbines). You will also
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cells (hPSCs) are particularly promising, as they can be differentiated into a diverse range of neural cell types. Our lab has successfully optimized protocols for the differentiation of hPSCs
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aim is to implement medical and synthetic chemistry experience to generate compounds with desirable biological activity, selectivity profile and optimal physicochemical properties. The position offers
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of its Remaining Useful Life (RUL). This paradigm shift allows manufacturers to switch from wasteful reactive maintenance to precise, condition-based interventions, optimizing resource use, extending fluid