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Applications are invited for a fully-funded 42-month PhD studentship with Dr Rachel Nicks and Prof Stephen Coombes on the Leverhulme Trust-funded project White Matter Computation: Utilising Axonal
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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employees). As an equal opportunity employer, the Leibniz-HKI is committed to increasing the percentage of female scientists and, therefore, especially encourages them to apply. For further information: Prof
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related field. Interested? Contact Prof. David Wagg (david.wagg@shef.ac.uk) for more information. About the Research Environment: The Dynamics Research Group in the School of Mechanical, Aerospace and Civil
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uses world-class big-data omics analysis of immune cells, including RNAseq, genome-wide epigenetics, multicolour flowcytometry, cell sorting, multiplex cytokine analysis, as well as classical
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machines that will lead to demonstrations with practical relevance. Specifically, this project in the group of prof. Feringa aims to address two key challenges: 1) How can we amplify the work of molecular
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with a systems biology approach in which we will investigate whether the immunological changes in young and elderly are characterized with changes in the gut microbial composition. Currently big data
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data requirements, and lower costs for large-scale modelling tasks. PINNs enhance predictive capabilities and efficiency by combining data-driven methods with physical principles. Unlike traditional
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PhD position - Stress-testing future climate-resilient city and neighbourhood concepts (Test4Stress)
also supervised by Prof. Dr. Jana Sillmann, who is co-leading the Research Unit for Sustainability and Climate Risks (FNK). This Research Unit is devoted to inter- and transdisciplinary research and
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computational models and data analysis code to process large, multimodal behavioral datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning