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at KTH in the Biotechnology doctoral program (according to KTH’s regulations). Signing a researcher agreement is a necessary condition to accept this PhD position. More information about Formulation of
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. This project will rely heavily on computational analyses, so experience on bioinformatics/informatics is required. The student will receive extensive training in big-data analysis, genome-wide/species-wide
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behaviour as the temperature drops, combining quantum mechanics, large-scale molecular dynamics, and deep learning. Work with world-leading researchers at Warwick and the Max Planck Institute for Sustainable
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will develop the core AI and data-driven models that transform large-scale exposomic and health data into actionable risk predictions and feedback systems. You will: Design spatio-temporal machine
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that come along with large-scale experiments. We offer leading interdisciplinary research between domain research fields and computer/data science or applied mathematics; research at world-leading, large
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trigger redesigns across multiple groups. The challenge is compounded by the fact that each discipline uses different data models and representations, making system-level interdependencies difficult
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years of postdoctoral research experience is preferred. Strong background in big data analytics, machine learning, and multi-omics. Strong track record of high-quality research, demonstrated by
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
observation, large, high-resolution datasets are now available for studying Earth’s climate, as well as the environments of Mars and the Moon. Extracting physical information from these datasets involves
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29 Mar 2026 Job Information Organisation/Company Mittuniversitetet Department Department of Natural Sciences, Design and Sustainable Development Research Field Environmental science Researcher
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recovery efficiency. Working closely with our experimental collaborators, you will analyse and model experimental data to efficiently explore the large design space of membrane functionalization and