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the immune response to mpox in both animal models and in vitro systems. We are looking for a motivated PhD student to help advance this line of research. As a PhD student, you will: Design and conduct
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Foundation Models initiative . The proposed starting date is 1 September 2025 or soon thereafter. The appointment will be made for a term of three years at a competitive salary and will follow the PhD study
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neurodegenerative diseases. The most promising candidates will be assessed using advanced docking and dynamics simulation techniques, providing insights into their potential interactions. To further substantiate
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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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deforestation rates, improved agricultural practices, reduced mismanagement of land use and enhanced community engagement #assist in the biophysical modelling system (link to ClimateBridge cluster) to local
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, including patch-clamp electrophysiology, high-resolution imaging, chemogenetic manipulation of glial activity, and behavioral assays in rodent models Analyze and interpret complex datasets from
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, and state-of-the-art infrastructure. Our model empowers visionary leaders to accelerate drug development and harness AI-driven innovation to address unmet medical needs. Benefits of Working with Great
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multidisciplinary team of students and postdocs. Interested? Check out our laboratory and publications on www.raeslab.org and/or https://scholar.google.com/cit... How to apply? Interested candidates should apply
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electrophysiology to translational models, including animal studies and analyses of human tissue samples. This full-stack methodology enables us to directly link molecular channel function with disease phenotypes
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with digital twin modelling, remote sensing, and cloud computing is valued, as is a commitment to learning and advancing in these areas. The successful candidate will have the ability to work with large