Postdoctoral Associate; R26-47

About RF for SUNY at the University at Albany:

Employment is with The Research Foundation for the State University of New York, a private, nonprofit, educational corporation that provides essential sponsored program administration and innovation support services to SUNY faculty and students whose ideas and research generate ground-breaking discovery and translate to entrepreneurial and economic development opportunities for New York State.

Working at the SUNY Research Foundation for the University at Albany, you will be part of a team that provides essential services to the University as it moves to the front ranks of American higher education, supporting the advancement of education, research and discovery. The University at Albany, a nationally recognized research university with outstanding faculty, researchers and staff, offers challenging, exciting and rewarding careers to those who seek opportunities to grow and excel.

The University at Albany is at the forefront of innovation and exploration in a wide range of research areas, from the health sciences and engineering to the humanities and fine arts. UAlbany has been recognized as a Carnegie R1 research institution - one of the elite "very high research activity" doctoral universities as ranked by the Carnegie Classification of Institutions of Higher Education.

This Postdoctoral Research Associate (PDRA) position works under the leadership of Professor Sukanta Basu. The incumbent will conduct research at the intersection of atmospheric science, Earth observation, machine learning, and power-system resilience, as part of a NASA-funded project focused on improving weather-driven power outage prediction.

Description of Duties:

• Develop and evaluate machine-learning architectures that integrate down scaled meteorological information from Prithvi-WxC with Prithvi-EO Earth observation embeddings and infrastructure information to predict spatially resolved power outage occurrence, magnitude, and restoration duration.
• Develop and evaluate methods for generating point-scale meteorological predictions at locations where direct observations are unavailable, including grid assets such as substations and transformers. Integrate down scaled meteorological fields, Earth observation embeddings, geographic and land-surface information, and observations from surface meteorological networks.
• Contribute to the development, testing, and evaluation of baseline machine-learning models for power outage prediction using bagging and boosting approaches. Help establish quantitative benchmarks for evaluating the performance of the Prithvi-OPM framework.
• Contribute to the development and evaluation of Prithvi-WxC-based methods for downscaling coarse-resolution numerical weather prediction data to high-resolution meteorological fields relevant to power outage applications. Participate in model training, validation, and evaluation of deterministic and probabilistic predictions.
• Contribute to the development and delivery of training materials, demonstrations, seminars, workshops, and short courses related to Prithvi-WxC, Prithvi-EO, and the Prithvi-OPM framework.
• Prepare peer-reviewed journal manuscripts, conference presentations, technical reports, software documentation, Jupyter notebooks, and other research products. Contribute to the open-source release and documentation of models, code, and computational workflows

• A Ph.D. in Computer Science, Data Science, Artificial Intelligence/Machine Learning, Electrical Engineering, Atmospheric Science, or a related quantitative field a college or university accredited by the U.S. Department of Education or an internationally recognized accrediting organization.
• Demonstrated knowledge and experience…