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Field
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: Experience with analyzing GPS tracks Good data-handling skills and ability to use R (compulsary) and preferably also Python and/or GIS competently Statistical/causal inference knowledge PhD degree in a related
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data (nationwide LiDAR coverage at 50 cm resolution). The candidate will perform quantitative morphometric analyses of landscapes and river networks near suspected active faults using GIS tools, Python
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
, there may also be opportunities to contribute to MIT courses on urban design, GIS, data visualization, and the relationship between technology (including AI) and the urban environment. A full job
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competitions including creating challenges Building integrated dashboards with any of the following tools: SQL Server, PowerBI, Tableau, Python/Machine Learning, AWS, Azure. Security clearance (current or
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systems (GIS). The PhD project should develop methods based on spatial indicators used to assess current and future climate risks. The IPCC's risk framework is suitable to be operationalized with spatial
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are looking for – Selection Criteria: Essential: Experience in basic GIS skills using QGIS or ARCGIS Demonstrated experience working in scientific report writing Experience working in data analysis using R
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business correspondence and documentation abilities. - Ability to manage confidential and time-sensitive matters. - Experience with Python and SQL. - Familiarity with network topologies, wireless
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, or a related field Strong experience in spatial and/or landscape modelling Proficiency in R and/or Python Experience with GIS and remote sensing Ability to work with large and heterogeneous datasets
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of the candidate Essential requirements: A 1st class or 2.1 degree (or equivalent) in Environmental Science, Remote Sensing, Computer Science, Surveying Engineering, or related field Strong coding skills (Python
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, or a related field Strong experience in spatial and/or landscape modelling Proficiency in R and/or Python Experience with GIS and remote sensing Ability to work with large and heterogeneous datasets