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-driven AI models that capture the underlying process–structure–property relationships governing metal additive manufacturing. By combining mechanistic modelling, in-situ sensing, and machine learning
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– you’ll nurture a structured and collaborative working environment Relationship management – you’re skilled at building partnerships and managing key accounts, working University-wide and externally Strong
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an annual stipend of up to £10,000 Supervision and guidance from leading experts in the field Structured research training at SOAS. Access to workspace and resources to attend and present at national
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Built Environment portfolio, particularly within our established and high-impact programmes in Quantity Surveying and Construction Management. This post will support the School’s broader strategic aims
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to elite contexts. Examine how organisational policies, cultures, performance structures, and social relations shape everyday practice and support or constrain high-level performance. Co-develop evidence
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, enabling opportunities for future funding applications and career development. Applications are encouraged from candidates with experimental backgrounds in physics, material science, biophysics, structural
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. More information on our structures and initiatives around EDI, including information on staff diversity networks, can be found on our Equality and Diversity Intranet page .
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concentrates on understanding complex data structures, investigating mismatches, identifying root causes of data issues and resolving them. You will: Analyse source and target system databases to understand how
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with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from
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: Resistance Analysis: Conduct simulations to analyze wave interactions, buoyancy, and stability of modular floating structures. Optimize designs for dynamic environmental conditions (e.g., waves, tides