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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive
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of Singapore's research and innovation ecosystem and aims to support the development of a sustainable, climate-resilient, and healthy city through data-driven design and policy-relevant research. The programme
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, this enables the development of more accurate computational models for various industrial processes. The experimental methods developed in this project will directly contribute to a better understanding of
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training and development opportunities. The expected start date is flexible but ideally from Autumn 2026. The initial appointment will be for one year, with the possibility of extension based on funding
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to the Lancaster Experimental Economics Lab (LExEL) ESRC NWSSDTP cohort training and interdisciplinary development Fully funded participation in national and international conferences Opportunities for teaching
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expertise computational model development. Candidates who have not yet acquired their PhD would be appointed at the Research Assistant level. What we can offer you: The opportunity to advance our
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this knowledge to suggest new ways to optimise catalytic reactions. Overall, you will contribute to the development of AI-enhanced autonomous workflows for interface. These implementations will be general and will
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Nature-based Solutions (NbS), with Zeeland as a core case study. The PhD will analyze sectoral development and adaptation, assess socio-economic impacts of NbS, study stakeholder expectations and
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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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formation, compromising the enhancements achieved through the use of TC intensification principles. The proposed project will investigate the development of TCRs by studying the impact of geometries and