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At the Technical Faculty of IT and Design of the Department of Sustainability and Planning, Copenhagen, a position as Postdoctoral researcher in Geospatial Machine Learning for Predicting Land Use
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Term: July/August 1, 2026 to June/July, 2027 (renewable) Appointment Start Date: August 1, 2026 (flexible) Group or Departmental Website: https://pedl.sites.stanford.edu/ (link is external) How to Submit
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platforms such as Cornell TEAM-Cities, CATChain, uTECH, etc. • Working with geospatial and mobility datasets (GPS trajectories, transit feeds, sensor data, demographic/socioeconomic data) • Co-designing tools
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mentoring of members of our research network, as well as outreach activities, all generally related to your research topic though not exclusively. You are encouraged to visit the ESA website: https
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environmental processes, in order to explain their long-term developments and to understand how they resonate into the present (https://www.uni-kiel.de/en/cluster-roots ). ROOTS combines expertise from a wide
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. Working with geospatial and mobility datasets (GPS trajectories, transit feeds, sensor data, demographic/socioeconomic data) Co-designing tools and analyses with municipal and MPO clients, including
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Preferred Qualifications: Experience working with Medicare data or other large administrative data sets Experience designing and implementing randomized controlled trials in the field Geospatial skills
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restoration measures. Prioritisation and optimisation of land for biodiversity, relative to other societal challenges, in particular climate change. Geospatial analyses of past and current land use patterns
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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. Where to apply Website https://www.academictransfer.com/en/jobs/357259/postdoc-air-quality-emission-sc… Requirements Specific Requirements A PhD in environmental sciences, geosciences, computational