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Field
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? Join an interdisciplinary PhD project that uses large-scale viral genome and epidemiological data to develop predictive theory for rapidly evolving viruses. You will work at the interface of theoretical
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estimates. The project will contribute new knowledge at the interface of computational fluid dynamics, atmospheric sensing, and environmental monitoring, and will provide tools directly relevant to improving
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forward allele-frequency diffusion and backward genealogical merging processes. The PhD candidate will work at the interface of machine learning, statistics, probability, and with applications in
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to enable quantitative, high-resolution, time-lapse monitoring of soil properties and will be implemented using high-performance parallel computing. This PhD position offers the opportunity to work at the
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and consumables, to begin September 2026. The PhD will be part of the Dublin Chemistry Graduate Programme. For further information or to apply please email a PDF copy of a brief cover letter and CV, to
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candidate will undertake a PhD project in experimental neurobiology at the interface of neuroscience and oncology, within the emerging field of Cancer Neuroscience. The research focuses on interactions
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will work at the interface of computational and experimental protein science. Your research will include: - De novo protein and enzyme design using computational and AI-based methods - Experimental
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 19 days ago
Framework Programme? Not funded by a EU programme Reference Number 2026-09927 Is the Job related to staff position within a Research Infrastructure? No Offer Description This 3-year PhD position is fully
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. This international PhD is part of the Collaborative Doctoral Partnerships programme, training researchers at the science-policy interface. Your profile Qualification and specific competences required Master's degree
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matrix (the “mortar”). Despite its importance, the detailed structure of the interface between these two components remains insufficiently understood. This PhD project aims to deliver a quantitative