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developing the theoretical and algorithmic foundations of compositional world models. A key application focus of the grant lies in rapid and safe real-world skill acquisition in application domains such as
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are looking for a researcher to drive the preparation and analysis of new and existing large-scale wearable sensor datasets. You will be an integral member of the wearables group based at Oxford, led by Aiden
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of Oxford. The post is funded by United Kingdom Research and Innovation (UKRI) and is for 24 months. The researcher will develop 3D mapping and reconstruction algorithms with relevance to mobile robotics
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” and “wet” lab workflows). You will be able to Design, develop and implement algorithms and systems based on foundation models, large language models and/or AI agents for automated scientific discovery
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Regularization. We aim to develop mathematical understanding of implicit regularisation properties in deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical
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, engineering, or a related field. Strong programming skills and experience in machine learning or statistical modelling are essential. Experience with healthcare data, algorithmic fairness, or deep learning
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such as UK Biobank. You will design and optimise scalable computational pipelines and algorithms to construct and evaluate foundation models for whole-body and abdominal MRI. Alongside this, you will
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frameworks, database architectures, and decision-support tools. The role also involves contributing to the design and implementation of AI algorithms, assisting in the development of research publications, and
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research for understanding the learned algorithms in brains and machines. The post holder will provide guidance to less experienced members of the research group, including postdocs, research assistants
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navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as