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Field
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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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to mentor student assistants and provide regular constructive feedback toward identified program goals. Willingness to learn more about digital scholarship tools and applications, such as Python, R, GIS, and
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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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) Requirements: 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
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California State University, San Bernardino | San Bernardino, California | United States | 3 months ago
proficiency in coding languages (e.g., Python, JavaScript, SQL) to design, code, test, and deploy custom digital applications and tools that enhance the community's offerings and data collection capabilities
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research environments are essential. Proficiency in ArcPro, Python and a solid grasp of planetary geological processes, especially those relevant to Mercury, will be key to your success in this role
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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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skills (e.g. R or Python) for data manipulation and visualisation, and to perform statistical analysis (e.g. mixed models). Knowledge of any of the following will be an advantage: geo-statistics, AI
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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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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