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Sharing – Building a federated data space to enable responsible data integration and cross-project learning. AI & Modelling – Using shared data to power advanced models that help describe and predict
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: 87392BR Supervises staff involved in providing clinical patient care. Assists in ensuring the quality of patient care delivery in a specific patient care area and serves as a role model for professional
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on the populations phenotypic plasticity and/or genetic adaptation. As our model system, we study springtails (Collembola), key terrestrial ectothermic invertebrates of the soil community. The postdoc will be mainly
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quality and climate. Our goal is to understand how these gases are produced and removed in peatlands and to bring this knowledge into climate models. Project links - https://villumfonden.dk/en/projekt
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and validation of a predictive pipeline for excipient–biologic interactions Integration of experimental SAXS data with AI-driven structural modeling to predict oligomerization behavior and excipient
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of Chemistry is a leading international centre for chemical kinetics and its applications. (https://eps.leeds.ac.uk/chemistry ) Task description for your Individual Research Project (IRP) Problem Definition
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computational modelling and non-invasive brain stimulation. The focus of this project will be on advanced versions of transcranial alternating current stimulation (tACS), targeting multiple brain areas in
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to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. The researcher will be part of the team of the MCCS NATURE Project (https
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this context, we are seeking to attract talented young researchers willing to develop and apply new computational models/methods based on multi-omics data integration in relation to cardiovascular, immunerelated
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measured depends on clinical decisions that vary between hospitals, physicians, and patient states. As a result, previous models have struggled to generalise beyond the hospital they were trained on. We