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operational safety levels of track and vehicles. Here, the project will take a hybrid approach in combining physical simulations and data driven analyses (featuring AI/ML as well as ‘traditional’ statistical
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appropriate statistical methods, contribute to scientific publications, project reports, presentations and other communication of research results, participate in project meetings and collaborate with academic
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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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, physicochemical and functional properties, process, analyse and interpret experimental data using appropriate statistical methods, contribute to scientific publications, project reports, presentations and other
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. Preferred qualifications A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline Experience of AI- or statistics-supported analysis
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Engineering and production optimization Documented mathematical, analytical and problem-solving skills. Good knowledge of statistics, AI/ML, and mathematical modeling and programming. Good knowledge in academic
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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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mechanical modelling, such as finite element analysis (FEA). experience of working with healthcare professionals and patients. strong knowledge of statistical methods, including non-parametric methods
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transport, urban planning, public policy What you will do Develop conceptual frameworks for mobility service usage Statistical analysis of surveys Qualitative interviews with residents in Gothenburg Supervise