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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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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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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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investigate HIV-induced vascular dysfunction and neuroAIDS molecular mechanisms using in vitro and in vivo (animal models) approaches. Specifically, the person will utilize HIV mouse models and HIV-1 infected
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- research experience in the evaluation of global wave reanalysis and integration of topographic and bathymetric datasets, as well as experience in the implementation of XBeach and SWaN models. Preferential
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of the SU(N) Fermi-Hubbard model and its low-temperature phases at the microscopic level. Share this opening! Use the following URL: https://jobs.icfo.eu/?detail=1003
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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
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an important entry point for data and models in the Metropolitan Region of Amsterdam. Where to apply Website https://www.academictransfer.com/en/jobs/357691/postdoc-micromacro-modelling
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self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed to irrigate