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geospatial modeling approaches to analyze urban environmental processes. • Apply machine learning and geospatial data analysis techniques to large Earth Observation datasets. • Produce high-quality
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Max Planck Institute for Political and Social Science, Göttingen | Gottingen, Niedersachsen | Germany | about 4 hours ago
input, fieldwork oversight, and analysis of large-scale cross-national survey data; Oversee key aspects of implementing an international teacher survey, including coordination with external survey firms
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technical reports, summaries, protocols, and qualitative analyses. Develops systems, processes, and procedures for timely data collection. Provides statistical analysis and data management support scientific
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demonstrated experience in computer vision or analysis of pathology images. The appointees will participate in a multidisciplinary collaborative research project related to development of deep learning model
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literature search and preregistered meta-analysis Coordinate MRI data collection with the technical team at the Leiden University Medical Center (LUMC) Perform fMRI preprocessing and statistical analyses
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-series analysis, and environmental data analysis. • Demonstrated experience in artificial intelligence, including machine learning and deep learning, applied to hydrological or water-related systems
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with the Research Coordinators and Project Managers to define analysis plans and ensure data quality for specific projects. Prepare analysis datasets, reports, and visualisations for both internal
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analysis of sequence and genome data (bioinformatics). Experience working with non-model organisms (animals), ideally marine invertebrates. Oral and written communication skills in English. Desirable skills
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7 Apr 2026 Job Information Organisation/Company Lodz University of Technology Department Faculty of Civil Engineering, Architecture and Environmental Engineering Research Field Engineering » Civil
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demonstrated experience in qualitative data collection and analysis and analysis of survey data, with a preference for candidates with a strong commitment to community-engaged and policy-oriented scholarship