42 data-visualization-analysis "https:" PhD positions at Aalborg University in Denmark
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levels. A central component of your work will be the collection and analysis of qualitative empirical data. The empirical work will be based on in-depth case studies of firms operating within the Danish
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building, task reallocation, skill demand, and productivity outcomes in China and Denmark. The expected design combines quantitative analysis (firm-level data, job postings, occupational task data, and
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, data analysis, and dissemination of research findings. The project combines perspectives from systemic innovation, integrated care, and health economics. The PhD student will collaborate closely with
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Learning and data analysis and are motivated to apply these skills to complex materials and production systems. You are familiar with polymer materials and processing, and you have knowledge of techniques
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o calibration and/or data assimilation o satellite geodetic and/or Earth Observation (EO) data processing o meteorological/hydrological data processing and analysis Strong interest in collaborative
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data sources (e.g., registry data, surveys, and organisations). Your competencies Digital methods such as machine learning based classification, computational text analysis, network analysis, web
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analyze temporal dynamics using time series methods and statistical techniques, and you will explore spatial variability using suitable modelling and data analysis approaches. A central task will be
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information about the department, please refer to the website https://www.dcc.aau.dk/ The PhD Fellow will be affiliated to either the research group Literature, media, and society (https://www.dcc.aau.dk
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Department has activities at both Campus Aalborg and Campus Copenhagen. For further information about the department, please refer to the website https://www.dcc.aau.dk/ . The PhD Fellow will be affiliated
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be motivated to conduct in-depth empirical research in close interaction with the port sector. Solid qualitative methodological skills are required, and prior experience with qualitative data