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, handling, and synthesising big data geospatial data sets from various data sources. Cutting-edge expertise in advanced statistical analyses of large data sets and strong knowledge of programming languages
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and conducting laboratory work. Insight into applied mathematics, linear algebra, process-based modeling, and soil health indicators. Experience with Python, applied statistics, and gradient-based
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simulation frameworks. Strong knowledge of probabilities and statistics. Experience in design and deployment of behavioral experiments and/or survey travel data collection in the field. Demonstrated
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. Candidates, who can identify themselves with the following competencies will be preferred: proven expertise in acoustics, statistical signal processing, and/or machine learning (AI) methods. strong analytical
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, establish their statistical properties, and evaluate performance on both simulated and empirical data. Beyond advancing econometric theory, the project aims to deliver practical tools for applied researchers