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Fully Funded PhD Studentship (Home Fees) Project Title: Developing Fair and Robust Cancer Risk Prediction Models Using Health Data Supervisor: Professor Angela Wood (Professor of Biostatistics and
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privacy constraints, robust solutions are essential. This PhD project will develop methods for building reliable medical imaging models that generalize across distribution shifts without retraining
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Many machine learning (ML) approaches have been applied to biomedical data but without substantial applications due to the poor interpretability of models. Although ML approaches have shown promising results in building prediction models, they are typically data-centric, lack context, and work...
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(SATURN CDT) Robust Structural Validation of Plutonium and Minor Actinide Wasteforms: Bridging Parametric Asymmetry through Bond Valence Analysis and Spectroscopy School of Chemical, Materials and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 14 days ago
entanglement on a robust, chip-scale architecture. 2. Background and Relevance to NASA This research directly supports NASA’s mission to develop disruptive technologies for the Integrated Network (IN) and
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poorly calibrated uncertainty when integrating multiple modalities. Decision-making under uncertainty: Current frameworks rarely translate uncertainty estimates into robust downstream decisions. Addressing
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and improve model transparency. The research will also consider model robustness, fairness, generalisability, uncertainty and responsible use of AI in women's healthcare. Expected Contribution
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and internal tooling where needed. Work closely with the Principal Investigator and lab members to support research projects, turning experimental workflows into robust, reproducible pipelines. Support
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techniques including behavior cloning, reinforcement learning, and VLA reasoning Train models that are robust to real-world challenges such as sensor noise, partial observability, contact dynamics, and
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structural design. (3). Realize hierarchical structural decoupling via dual-network engineering to break the trade-off between mechanical robustness, wave absorption, and thermal conductivity. (4). Investigate