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will work with existing FH patient cohort data sets from France, Portugal, Czech Republic, and Turkey, which will be expanded with multi-level multi-omics data, including genetic, transcriptomic
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the fundamental understanding of pesticide residues in soils through experimentation, modelling, and big data analysis. You will: Combine existing farm management, soil property and soil pesticide residue data
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knowledge graph models of such transformations that are self-describing when applied to existing map repositories, and can be scaled up to large data repositories using state-of-the-art AI methods. In
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knowledge of extracting (event) data from text, documents, and/or media articles or implementing large-N analytics and algorithms, preferably LLMs and/or NLP. Conduct collaborative and independent research
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-engineering. A semantic interoperability backbone and data management are also required due to the large number of stakeholders and their need to receive or exchange information. Experience with modelling
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/nature conservation or a similar relevant field as mentioned above; Sound understanding of forest ecology and biodiversity; Experience or strong interest in big data analysis; Proficiency in programming
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. You will join our large team of about 30 researchers that focuses on the molecular aspects of infection of corona-, influenza- and picornaviruses. We offer a challenging position in a dynamic and
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the process level and the ecosystem level, which we assess with mixed methodologies like integrated assessments, the analysis of big data, precision agriculture, sensing, and robotics. We do this by means
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the interdisciplinary research programme GroenvermogenNL, which supports the large-scale development and implementation of green hydrogen in the Netherlands and across Europe. The programme addresses the system-wide
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programming, statistics, machine learning and big data approaches in the context of soil-vegetation-atmosphere interactions excellent writing and oral communication skills in English and strong ambition