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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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pipelines and algorithms to construct and evaluate foundation models for whole-body and abdominal MRI. Alongside this, you will conduct comprehensive and systematic literature and database searches related
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Bioinformatics and Computational Biology headed by Ivo Hofacker. Our team works on the development of algorithms and methods for problems in Computational Chemistry, Systems Chemistry, and Computational Biology
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; Preparation of gel composites; Thermal analysis (thermogravimetric analysis and differential scanning calorimetry); Microscopy (SEM, AFM); Crystal engineering of chemical sensors; Development of new
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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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Analysis for Stable AI” funded by a European Research Council Advanced Grant. The Opportunity: This postdoctoral position will be devoted to research on algorithms in Artificial Intelligence (AI) from
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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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experimentally validate sensors and AI software for medical imaging, 2) Integrate pioneering and proven tools for the early cancer detection, and 3) Examine clinical workflows and validate the established systems
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classification algorithms including machine learning); and the output data and interpretability. The project “SORS in the community” is funded by the EPSRC (https://www.ukri.org/news/new-tools-aim-to-improve-early