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relevant to mammalian cell culture systems, flow cytometry and immunogenicity assays. You will have prior experience of working with animal models. You will be able to work precisely and reproducibly and
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. The Department of Mathematics and Computer Science, Hessain.AI research group “Artificial Intelligence – Deep Decision Support Systems”, led by Prof. Dr. Martin Becker, is currently accepting applications for a
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understanding of geomagnetic field evolution across different timescales, including both stable and extreme periods. This will involve working with data-based models and numerical dynamo simulations
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that will be valuable for this position include computational modeling of enzymes, biophysical characterization techniques, and bioinformatics methods. The successful candidate will be able to work
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the current negative impacts. The Postdoc will focus on advancing novel fish detection technologies to support fish monitoring and more selective and sustainable flatfish fishing. This Postdoc project is
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community is fully international, with more than 50 countries represented. OIST is rapidly gaining recognition in the worldwide academic community as a model for excellence in education and research. Position
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experience in reservoir simulation and coupled geomechanical modeling. • Experience with CO₂ storage, CCUS, or subsurface fluid flow modeling. Desirable Education and/or Experience • Experience with NRAP tools
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: transport properties and selectivity. Monitor and analyze system performance: current efficiency, ion separation rates, energy consumption, etc. Model ion transport and system behavior under different
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their thermophysical properties. Proficiency in computational fluid dynamics (CFD) simulation of thermal energy storage systems, enabling the modeling and analysis of heat transfer, fluid flow, and thermodynamic
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highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud