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experience of building energy simulation and/or urban building energy modelling (UBEM). Strong programming and data-analysis skills (e.g. MATLAB, Python or equivalent). Very good command of spoken and written
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will demonstrate: A Masters or PhD in engineering, environmental science, or a closely related discipline Strong analytical and numerical capability, including experience with modelling, GIS and coding
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of hydrologic processes, transboundary/shared water resources management, and coupled human–natural systems. Proficiency in geospatial and scientific computing tools (e.g., Python, R, GIS, Google Earth Engine
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measurements. Proficiency in programming languages and data analysis tools such as Python, Matlab, and R. Proficiency in GIS software (e.g., ESRI ArcPro). Hydraulics of flood control and storm surge suppression
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presentations You have basic knowledge in data analysis using R, Python, MATLAB, or equivalent software You have basic knowledge in GIS and digital mapping You have the required communicative skills to interact
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analytical and quantitative skills, including statistical programming (e.g., R, Python, or similar). Knowledge of causal inference methods and/or comparative risk assessment approaches. Familiarity with
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 months ago
geospatial data processing and python programming. Candidates should also have knowledge of optical, lidar, and ground penetrating radar sensing systems and understanding of pavement structures and condition
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applying statistical or analytical methods using software such as R, SAS, Python, Stata, or similar tools. Familiarity with public health surveillance systems, environmental health data, geographic
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. Contribute to geospatial epidemiology analyses using GIS-linked eRegistry data. Develop reproducible R-based analysis pipelines, including support for DataSHIELD or other privacy-preserving/distributed
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research for an NIH-NSF funded project on human health, movement, and infectious diseases. Successful applicants will have experience in: Either spatial analysis using GIS or disease modeling in R and/or