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are seeking a postdoctoral researcher specializing in digital technologies and urban health. The successful candidate will develop innovative solutions based on the analysis of urban data (big data, IoT, GIS
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diseases. The successful applicant will join a number of fascinating projects on engineering novel approaches to modulate physiology and neural activity in the brain, gastrointestinal (GI) tract, and other
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teaching academic courses related to urban heritage, spatial justice, or affiliated domains. Required Qualifications PhD in Architecture, Urban Studies, Geography, Anthropology, History, or a related
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achievement of these objectives. Where to apply E-mail catherine.marchand@umontpellier.fr Requirements Research FieldGeosciences » HydrologyEducation LevelPhD or equivalent Skills/Qualifications PhD in
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Oldenburg Oldenburg, Niedersachsen | Germany | about 13 hours ago
of Oldenburg Your Profile PhD in marine ecology, finalized by the start of the project Advanced statistical modeling skills including analysis of biodiversity time series and functional traits, evidenced by
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. Required Qualifications A PhD or doctorate is required GIS, and experience in statistical modeling using SAS or R Location Hybrid/Flexible Work Arrangement – Philadelphia, PA Additional Information
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testing and stakeholder engagement. Collaborate closely with the PhD researcher to connect environmental data analysis with computational design innovation. Participate in fieldwork in Norwegian glacier
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Job Summary Are you passionate about applying cutting-edge GIS Mapping and Water Quality modeling techniques to address critical environmental challenges? Join us in an exciting initiative aimed
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We are seeking a Postdoctoral Researcher in Geospatial Urban Big Data to support the analysis of urban dynamics and decision-making in territorial planning. The candidate must be proficient in GIS
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data Experience with GIS/RS and database environments (e.g., ArcGIS and Quantum GIS) Experience with machine learning and statistical learning Experience working with large, diverse datasets Familiarity