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from animal hosts (are ‘zoonotic’), yet it is hard to predict which pathogen species are likely to cross into human populations and why. This project will combine AI Large Language Models, extracting
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. In this project we will study the functions of the rare coding variants using zebrafish and compare the results with mouse models of equivalent mutations and human post-mortem brain tissue
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Primary supervisor - Dr Thomas Anthony Haynes Pellet-clad interaction (PCI) is a complicated phenomenon, relatively poorly captured in current commercial fuel performance codes. Models have been
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with ordinary facial movement. While many methods have been developed to recognise and localise micro-expressions, these results remain difficult to understand. The proposed models will be designed
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design rules to understand their chemistry and physics. You will combine coarse-grained and atomistic simulations with surrogate models and experimental insights (with Dr. Baumgartner) to understand
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Models at the Department of Computer Science, Norwegian University of Science and Technology (NTNU). The position offers the opportunity to work on cutting-edge research at the intersection of deep
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student and postdoc who will carry out experimental work, which will allow for validation of the models. You will collaborate with experts from TU Delft, TNO and several large and SME companies involved in
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generative models of how human tissues develop, mature and regenerate, and of what goes wrong in disease, with a focus on child health. The group starts in January 2027. You will develop generative models
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23rd September 2026 Languages English English English The Department of Computer Science has a vacancy for a PhD Candidate in Efficient Edge Intelligence Models Apply for this job See advertisement
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Your position You will develop generative models on single-cell and spatial genomics data, from sequencing and imaging, and apply them to human developmental and disease biology. Possible research