Develop and train machine learning models for cloud microphysics, storm evolution, and weather-seeding impacts. Build data pipelines using balloon, radar, satellite, and model data; lead preprocessing, training, evaluation, and uncertainty experiments; and translate research into production forecasting tools. Collaborate with atmospheric and hardware teams on field integration, applying spatiotemporal, physics-informed, or time-series methods and distributed computing.
Roles & Responsibilities
Develop and train ML models for cloud microphysics, storm evolution, and seeding impact.
Build pipelines for ingesting balloon, radar, satellite, and model data.
Own experiments: preprocessing, training, evaluation, uncertainty.
Convert research into production-ready forecasting tools.
Work closely with atmospheric + hardware teams on data and field integration.
Strong ML research background (spatiotemporal, physics-informed, or time-series).
Experience training large models or working with distributed compute.
Comfortable reasoning from first principles and designing experiments.
Fast, independent, and thrives in a small, high-velocity team.
Passion for climate/weather.
Bonus: WRF, LES, data assimilation, microphysics knowledge.
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