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rely heavily on manual expert curation, limiting their scope and scalability. By combining AI-assisted information extraction with expert validation, this project will create high-resolution dietary
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molecular and clinical datasets, and world-class AI and experimental research environments. This combination provides a unique foundation for ambitious research and enables biological questions to be explored
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of designing and utilizing molecularly imprinted polymers (MIP). The group combines fundamental materials research with the aim of bringing MIP as close as possible to commercial applications. It operates in a
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cancer development, progression, and metastasis. The successful candidate will combine advanced cell and tissue culture methods with bioengineered model systems, quantitative microscopy, and molecular
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About the lab The Laboratory of Causal Systems Immunology combines causal inference, probabilistic AI and large-scale in vivo perturbation experiments to uncover how genes shape immune-cell states
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About the lab The Laboratory of Causal Systems Immunology combines causal inference, probabilistic AI and large-scale in vivo perturbation experiments to uncover how genes shape immune-cell states
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linking intermittent hypoxia to cardiovascular dysfunction in obstructive sleep apnoea (OSA), a common clinical condition associated with cardiovascular risk. The project combines in vivo cardiovascular
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mechanisms underlying protein–RNA interactions and their regulation. We combine structural, biophysical, cellular and computational approaches to understand how RNA-binding proteins recognise their partners
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of the future will increasingly rely on integrated digital systems that combine practical expertise with real-time feedback and predictive insights. At BFFI, we aim to improve CEA system design and crop
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immunotherapies for cancer patients with brain metastasis. Our group works at the interface between immunology and cancer biology combining the use of preclinical models of cancer and human samples with state