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aims to develop formal frameworks and algorithms for eliciting, aggregating, and analysing stakeholder preferences over risk and safety in AI systems. The Research Assistant will support the development
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on transplant using multimodal medical data. You will be responsible for literature review, data cleaning, model development and implementation. You should possess a relevant PhD (or near completion) in
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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fundamental algorithms for producing policies for rich goal structures in MDPs (e.g. risk, temporal logic, or probabilistic objectives), and modelling robot decision problems using MDPs (e.g. human-robot
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machine learning methods to improve the understanding, treatment and prevention of human disease. The successful candidate will develop novel statistical and machine learning algorithms to address key
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developing new algorithmic approaches for TAPS data, interpreting the results in the context of phenotypic observations, and communicating these findings clearly to the broader team. You will prepare the
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developed goal-sequence generalization task. The project will integrate high-density silicon probe recordings, optogenetics, pharmacology and advanced computational tools to analyse neural algorithms
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type (iv) work with the computational biology team to transfer this information into a AI algorithm that can distinguish neurodegenerative and neuroprotective phenotypes (v) work with colleagues in
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endoscopists through a quantitative and qualitative assessment of the mucosal microstructure. You will be responsible for the algorithm development and implementation of the overall system. It is expected