From deep mantle convection to hurricane intensification, Georgia Tech’s MS EAS arms students with remote-sensing toolkits, HPC climate models, and field campaigns on coastal drones. Cross-college electives in policy and machine learning ensure graduates can brief legislators and optimize ENSO neural forecasts alike.
AI nowcast of severe convective storms from GOES imagery
Seismic ambient-noise tomography of urban bedrock for infrastructure planners
Coupled ocean-atmosphere model evaluating offshore wind wake effects
GIS vulnerability map combining SLR and social equity indices
Dust aerosol radiative forcing analysis using MODIS and MERRA-2
Landslide early-warning IoT sensor network in Appalachian foothills
Inverse modeling of mantle plume temperature from hotspot chains
Atmospheric river forecast skill improvement with ensemble ML
Mars dust-devil detection pipeline for Perseverance rover data
Isotope geochemistry study of paleo-monsoon shifts in speleothems
Downscaled flash-flood risk atlas for urban basins
Methane super-emitter detection via hyperspectral cubesats
Glacial isostatic adjustment model for Antarctic grounding zones
Citizen-science air-quality app with low-cost PM sensors
Policy brief quantifying co-benefits of soot reduction on health
Model Earth systems and inform resilience on a warming planet.
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