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Field
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Thrive Global and two international partners in the United States and Sweden have been funded to deliver a new research programme, titled Collective Action for Race Equity in Health and Social Care (CARE
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create a working framework that includes both experimental and modelling prototypes, including AI/ML tools to assist with the large number of variables involved. This project is seeking candidates with a
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One fully funded, full-time PhD position to work with Alessandro Suglia in the Embodied, Situated, and Grounded Intelligence (ESGI) group at the School of Informatics, University of Edinburgh
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quantitative data, including spreadsheets, emissions databases, or modelling tool possess strong analytical and communication skills, with the ability to engage and collaborate effectively with both academic
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
to cutting-edge facilities including High-velocity impact testing, Advanced composite manufacturing labs, X-ray computed tomography and High-performance computing resources for AI model training This project
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position (2 + 2 model) under the supervision of Sebastian Szyller. Trustworthy & Adversarial Computing Lab is a newly established group. The lab has an extensive network consisting of companies and academic
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, are crucial for modelling volcanic processes and are vital for understanding the transitions. Geophysical monitoring provides essential information to constrain these parameters and inform decision
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models. The project’s key objectives are to: 1) Identify critical indicators relating to ecosystem health and resilience; 2) Incorporate indicators into DBN models to simulate how ecosystems respond
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(optional). Person specification: Prior experience in computer coding (e.g., Python, SLiM), AI modelling, and understanding of evolutionary or conservation genetics / genomics is desirable. Good teamwork
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intensifying upper ocean mixing processes. However, major gaps in our understanding remain due to challenges in observing and modelling the Arctic Ocean. Research Methodology The aim of this project is to