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research assistants, postdoctoral researchers, and academic staff to develop cutting-edge methodologies. The research is cross-disciplinary, combining advanced quantitative analysis, simulation, and systems
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research assistants, postdoctoral researchers, and academic staff to develop cutting-edge methodologies. The research is cross-disciplinary, combining advanced quantitative analysis, simulation, and systems
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research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus on ensuring that our research results
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of very large data sets, data analysis, and simulations of X-ray scattering and spectroscopy signatures of dynamic processes in battery materials. The theoretical/ simulation efforts are supported by
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. Qualifications Applicants with experience in mixed methods, particularly digital methods, data scraping/analysis and qualitative research (Netnography and interviews) will be preferred. Proficiency in English and
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cells, electroporation, multi-color flow cytometry, droplet digital PCR, DNA sequencing including NGS, viral vector production, and mouse xenotransplantation experiments. Analysis, interpretation, and
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, specifically within breast cancer and breast cancer survivorship. Make applications as needed for access to Danish population-based and medical registries. Independently perform statistical analysis using
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and reduction of very large data sets, data analysis, and simulations of X-ray scattering and spectroscopy signatures of dynamic processes in battery materials. The theoretical/ simulation efforts
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. The ideal candidate will demonstrate: Proven experience in thermodynamic modelling, thermo-economic analysis, and multi-objective optimization. Strong publication record in thermal conversion systems (e.g
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experience in quantitative modelling, preferably using R. Is fluent in English (spoken and written) and has good communication skills. Is enthusiastic about developing replicable models to address complex risk