POSTDOCTORAL ASSOCIATE, Mechanical Engineering, will work under the direction of Prof. Sherrie Wang to develop deep learning methods for hyper-local weather forecasting with uncertainty quantification. The position is supported by a NASA-funded project focused on probabilistic downscaling of global weather models using satellite remote sensing, generative AI models, and conformal prediction. The research integrates numerical weather prediction (NWP) outputs, weather station observations, and satellite data to generate accurate, uncertainty-aware local forecasts for applications in disaster response and energy systems. Will develop and implement machine learning models for local weather forecasting and uncertainty quantification, including probabilistic and generative approaches; integrate and analyze heterogeneous datasets, including numerical weather prediction outputs, weather station observations, and satellite remote sensing data; design and run experiments to evaluate model performance and generalization across locations and conditions; contribute to the preparation of manuscripts, technical reports, and presentations for scientific and sponsor-facing dissemination; collaborate with project team members and external partners to align research with application needs in energy and disaster response; mentor graduate and undergraduate students and contribute to a collaborative research environment; and participate in project meetings and related research activities as needed.

REQUIRED: Ph.D. in computer science, electrical engineering, atmospheric science, or a related field with a strong background in machine learning, statistical modeling, or geospatial data analysis; experience with deep learning methods for spatiotemporal data; experience working with large-scale datasets, ideally including remote sensing or weather/climate data; strong programming skills in Python and experience with ML frameworks; and familiarity with uncertainty quantification methods.

4/22/2026