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Field
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familiar with spatial analyses, species distribution modelling (SDM), and programming in the R or Python languages to apply these to: Collate and analyse spatial data on MPA boundaries, their protection
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, tracking, spatio-temporal modeling, edge learning) using field data (traffic cameras, micromobility telemetry, connected vehicle/V2X, crash records, GIS). · Manage and coordinate pilot deployments
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of dendrochronology techniques, from the research plan, field work and laboratory, and interpretation and publication. He/she should be comfortable with the R and/or Python languages. Experience in handling large
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analysis, GIS and Python regarding urban and regional planning Documented experience of working with urban and regional policies and qualitative data. Documented experience of working with transport related
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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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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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/C++, FORTRAN and/or Python. Experience working with geo-spatial information, remote sensing data, and GIS software. Experience in deep learning and computer vision. Experience in developing software
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, Python, Julia, etc. Demonstrated experience working with spatial epidemiological, ecological, or environmental data, including hands-on use of GIS, spatial statistics, or other spatially relevant methods
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: Valid driver’s license. Programming and numerical analysis skills (e.g., Python, MATLAB) and/or experience with groundwater/geochemical modelling software (e.g., MODFLOW, PHREEQC). Experience with
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planning. Coursework or research experience in road safety, traffic modelling, or transportation planning. Proficiency in working with open data sources for traffic and transit operations using R, Python