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Field
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environment data for Singapore from government agencies and open sources, using GIS software (i.e. QGIS), R, and Python. Undertake 2D and 3D geospatial modelling and analysis. Apply spatial and statistical
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coding. Datasets will be shared with the project team. The successful candidate will work with academic and stakeholder partners to generate model-ready UAV datasets and develop evidence to support the
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Do you enjoy finding solutions to integrate and analyse large data sets of biodiversity dynamics and their drivers? Are you creative and able to couple various data flows and integrated modelling
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for modelling complex international scenarios, allowing policymakers and scholars to anticipate the consequences of strategic decisions, trade agreements, or environmental regulations. Geospatial
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and compound flood modeling (rainfall-driven and wind-driven) and geospatial decision-support systems to advance the goals of the Rhode Island Natural Hazards Collaborative, and will serve as the
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experience developing deep learning models using frameworks using PyTorch and possess strong Python programming skills. Experience working with Remote Sensing datasets and geospatial technologies such as GDAL
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learning materials for researchers at different levels of technical experience. Advanced AI for research: Provides instruction and consultation on large language models, prompt and context design, model and
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human, cultural, and physical systems. Geography seeks to study and understand the distribution, dynamics, and interrelationships between humans and their physical environment. Geospatial Sciences applies
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causal inference, statistical modelling, game theory, machine learning, natural language processing, geospatial analysis, computational text analysis, or large-scale data analysis. Proficiency in relevant
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. Excellent analytical skills with high attention to detail. Experience with geospatial data visualization tools such as ArcGIS, Cesium, or similar. Familiarity with urban-scale building energy modeling