Postdoctoral Associate (R26-47)

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 downscaled 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 downscaled 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

Job Requirements:

Minimum Requirements:

• 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 in artificial intelligence and machine learning.
• Experience with deep-learning frameworks such as PyTorch, TensorFlow/Keras, or JAX.
• Knowledge of foundation models and their application to scientific or engineering problems.
• Advanced programming and scripting skills, preferably including Python.
• Experience working in Linux-based computing environments; familiarity with high-performance computing and/or GPU-accelerated computing is desirable.
• Ability to collaborate effectively as part of an interdisciplinary team and work with minimal supervision.
• Demonstrated ability to conduct independent research and disseminate results through peer-reviewed publications, conference presentations, or technical reports.
• Strong oral communication skills, including the ability to communicate technical results to scientific and non-technical audiences.

Preferred Requirements:

• Experience processing and analyzing large scientific, geospatial, meteorological, or other multidimensional datasets.
• Experience with geo-foundation models.

Additional Information:

Salary Range: $62,500-$65,000 (DOE)

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The Research Foundation for The State University of New York offers exceptional benefits such as healthcare, dental, vision, pension plans, competitive pay, generous paid time off, tuition assistance, life insurance and long-term disability insurance. The University at Albany offers a variety of performing arts, world class libraries, and competitive sporting events.