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exists for researchers to design and improve animal tests. These limitations hinder the development of optimal experiments and incur cruel animal suffering and killing.The position is two years and you
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repertoire sequencing to examine vaccine-induced antibody and B cell responses in rituximab-treated multiple sclerosis patients across three doses of the BNT162b2 mRNA vaccine. The goal is to identify key
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detailed catalyst characterization where APT (atom probe tomography) is a key technique. The catalyst will be tested in high hydrogen pressure reactors and the gas will be analysed with GC. A central part of
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to the development of advanced methods for electrode fabrication, with a particular emphasis on electrophoretic deposition. You will also conduct electrochemical testing under applied magnetic fields to improve
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tested in high hydrogen pressure reactors and the gas will be analysed with GC. A central part of the work will be detailed catalyst characterization. A wide range of methods will be used, including XRD
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contribute to the activities including TES unit development, laboratory testing and techno-economic analysis to identify optimal integration opportunities. Cooperation with industrial and academic national and
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/discharge testing Conduct post-mortem analysis using SEM and XPS Prepare synchrotron beamtime proposals and carry out operando studies with X-ray tomography Analyze experimental data, prepare reports, and
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cells and perform electrochemical characterization, including EIS, DRT, and charge/discharge testing Conduct post-mortem analysis using SEM and XPS Prepare synchrotron beamtime proposals and carry out
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of protective materials Perform validation studies to ensure computational model accuracy against experimental benchmarks Collaborate on the development of testing methodologies for protective device evaluation
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model accuracy against experimental benchmarks Collaborate on the development of testing methodologies for protective device evaluation Machine Learning Integration Develop and implement machine learning