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atomic- and microstructural-scale characterizations, with the objective of establishing robust structure–property–adhesion relationships. Overall,this project will enable engineering thin film stacks with
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PhD Studentship: Development of a robust hydrological modelling framework for drought risk assessments Award Summary Tax-free annual living allowance £26,546 a £20,000 research training grant and
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03.06.2021, Academic staff The Albarqouni lab develops innovative deep Federated Learning (FL) algorithms that can distill and share the knowledge among AI agents in a robust and privacy-preserved
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rapid, data-driven, physically informed and human-aligned decision framework capable of robust, equity-aware intervention planning under climate uncertainty. This PhD will develop a decision-support
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properties. Advanced fabrication techniques, including solution casting, electrospinning, and green solvent processing, will be explored to create thin, robust, and conformable membranes compatible with
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-scale ML more reliable, transparent, and aligned with human values. We are specifically interested in: Data-centric AI: Advancing machine unlearning, privacy-preserving techniques, and robust data
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-scale ML more reliable, transparent, and aligned with human values. We are specifically interested in: Data-centric AI: Advancing machine unlearning, privacy-preserving techniques, and robust data
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, accelerating pharmaceutical development and ensuring robust product performance. Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a
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addresses the "calibration problem" in particulate continuum models and particle simulations. Specifically, it focuses on developing robust methodologies for selecting and parameterising contact models, a
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and decision support (e.g., LLMs, retrieval-augmented generation, agentic systems) Building production-quality prototypes suitable for pilot deployment in judicial settings, including robust retrieval