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, and data-driven analysis. The project will be supported by a strong research environment with experience in concrete behaviour at elevated temperatures, constitutive modelling, and advanced numerical
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its frontier by integrating mechanistic artificial intelligence with robotic additive manufacturing systems to enable intelligent metal processing. The research will develop physics-informed and data
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be conducted beginning May. Contact for enquiries Informal enquiries may be addressed to Prof Yafa Shanneik (ys32@soas.ac.uk ). For more information: RELI-GENE: Governing Health, Family and Religion
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training will be provided. We particularly welcome applicants who are excited about integrating ecological understanding with data-driven methods. There is flexibility to tailor the research to your
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for ‘Course Title’ using programme code: 8050F Research Area: Computing Science select ‘PhD Computer Science’ as programme of study You need to provide the following information in ‘Further Questions’ section
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select ‘PhD Computer Science’ as programme of study You need to provide the following information in ‘Further Questions’ section: ‘Personal Statement’ (mandatory) - upload document or write statement
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is 2:1. Degree in psychology, public health, epidemiology or other health-related discipline. Mode of study: Full-time Start date: 1 October 2026 Additional Funding Information This project is fully
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are emerging as promising alternatives, offering potential benefits in carbon reduction, durability, and ageing resistance. However, widespread adoption is limited by inconsistent performance data, insufficient
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should hold a Master’s degree in Economics or a related field (e.g., Finance, Psychology, Maths or Data Science), with a foundational understanding of behavioural/experimental methods and/or econometrics
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the ergogenic aid. Further information: Applicants should have at least a 2:1 in a relevant degree (for example but not limited to Sport Science/Medicine/Rehab, Public Health, Medicine), and ideally a relevant