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or scale-up of algal cultures; biomass harvesting and characterization; trace-element analysis; geochemical or mineralogical methods; and statistical analysis of multivariate experimental data. Personal
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record in relevant peer-reviewed journals, including publications demonstrating a substantial individual contribution. Experience with scientific programming, experimental automation, and data analysis
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. Experience with scientific programming, experimental automation, and data analysis using, for example, Python, MATLAB, or LabVIEW. Experience with numerical modelling of nonlinear or integrated photonic
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. The work may include experimental research in laboratory and industrial settings, system development, analysis of production flows, and co-creation of knowledge together with company partners. As MARC is a
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battery-related hydrometallurgical leaching as a model system, the project combines controlled experiments, real-time monitoring, chemical analysis, and data-driven modelling. The postdoctoral researcher
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research environments in Computational Science, the research and education has a unique breadth, with large activities in areas such as numerical analysis, mathematical modelling, development and analysis
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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-edge detectors and provide data processing, visualisation and analysis solutions that enable researchers to rapidly interpret results from X-ray scattering, imaging and spectroscopy experiments. Our
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LC-MS/MS method for their analysis in different matrices. The method is further developed by including other fermentation-derived metabolites and is used for analysis of thousands of samples in
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strong scientific background with relevant expertise in cell and/or molecular biology. Interest in programming, computational biology and statistic towards high-throughput data analysis is considered a