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modelling tools are required. Robust modelling and programming abilities (e.g., Python) are essential prerequisites. Experience with VIC (or similar hydrologic models), GIS, and large-scale computing
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years prior to the effective date of appointment with a minimum of one year eligibility remaining. • Strong proficiency in Python or R and experience with High-Performance Computing. • Proficient
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-solving abilities. Strong written and oral communication skills in both English and French. Additional Skills (Preferred): Experience with environmental data analysis (R, Python, multivariate statistics
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; GIS software; spatial analysis and visualization; programming in R, Python, or similar; quantitative data collection and analysis; scientific synthesis and writing. Experience partnering with non
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hydro-climate studies. Strong background with GIS tools and spatial analysis techniques. Demonstrated expertise in climate variability assessment and the use of climate models. Experience with
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Andres Masegosa (arma@cs.aau.dk), Department of Computer Science. (please see: https://andresmasegosa.github.io/ . The project’s domain PI is Professor Jamal Jokar Arsanjani (jja@plan.aau.dk), Department
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GIS software and Python, strong written and interpersonal communication skills, and a demonstrated interest in addressing social justice issues through data-driven research. The postdoc will work in
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, geospatial analyses and/or statistical modelling, satellite data retrieval; preferably experience with Pascal programming language and R or Python and geographic information systems (GIS); a proactive attitude
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qualifications in probilistic risk modelling, applied statistics and familiar with quantitative risk modelling measures strong data analysis skills and proficiency in R and Python; experience with other programing
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Date: As soon as possible Group or Departmental Website: https://naturalcapitalproject.stanford.edu/ (link is external) How to Submit Application Materials: Send an email with your attachments