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SWAT+gwflow Proven experience in modelling natural and human-induced drought (streamflow and groundwater drought). Proficiency in programming (R, Python, or MATLAB) and GIS tools. Experience in climate
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biochemical models, data assimilation, spatial analysis and GIS approaches. • Programing skills (e.g. R or Python) for data manipulation and visualisation, and to perform statistical analysis (e.g. mixed models
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the Collaborative Doctoral Partnerships programme, training researchers at the science-policy interface. Where to apply Website https://jobs.unibas.ch/offene-stellen/phd-position-ai-driven-pathways-to-health
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Information Science (GIS), and computational science for health and environment, to study processes spanning from the microscopic to the planetary, across all time scales. The Inverse Modelling group at the Department
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for the materiality of the built environment in defined regions, based on MFA and supported by BIM, GIS, IoT and AI technologies. Map existing anthropogenic material stocks and their dynamics and simulate circularity
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programming skills (e.g., Python, Ruby, PowerShell). A good working knowledge of Artificial Intelligence (AI) principles. Including LLMs and the use of AI Agents Experience working with GIS systems and
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for the materiality of the built environment in defined regions, based on MFA and supported by BIM, GIS, IoT and AI technologies. Map existing anthropogenic material stocks and their dynamics and simulate circularity
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hours; Health insurance, employee benefits, and life in a pleasant and safe city; Competitive salary. Where to apply E-mail vladimir.remes@upol.cz Website https://pracuj.upol.cz/nc/zprava/clanek/postdoc
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, and interprets cancer surveillance, population, geographic, environmental, and health services catchment area data using appropriate data management, statistical, GIS, and other programming software
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supervision The following experience will strengthen your application: Advanced coding skills (Python, R, etc.) Expertise in GIS and data visualisation. Experience applying Machine Learning, particularly