Postdoctoral Researcher – Vision Machine Learning

Posting Title Postdoctoral Researcher – Vision Machine Learning.

Location CO - Golden.

Position Type Postdoc (Fixed Term).Hours Per Week
40.Working at NLRNLR is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development.

Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions.

Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.

At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.

Job Description The National Laboratory of the Rockies (NLR) is seeking a Postdoctoral Researcher to conduct research at the intersection of machine learning, computer vision, large-scale data analytics, and photovoltaic (PV) system performance . The successful candidate will join a multidisciplinary research team working to develop new methods for understanding how the U.S. PV fleet changes over time.

A primary focus of this position will be developing and applying computational methods to identify and characterize changes in utility-scale PV systems using satellite and aerial imagery, large operational datasets, and other geospatial and system-level data . The researcher will develop, adapt, and validate algorithms for image-based identification and change detection and integrate these results with large-scale PV performance datasets.

The successful candidate will work closely with researchers in NLR's PV performance and reliability research community and contribute to the PV Fleets research portfolio. The position requires a researcher who is comfortable developing computational methods, working with large and heterogeneous datasets, evaluating algorithm performance and uncertainty, and translating research methods into reproducible scientific software and analyses. The researcher may also contribute to related projects involving PV fleet evolution, repowering and decommissioning, PV end-of-service analysis, material-flow modeling, and PV circularity.

Specific research activities may include:

Developing, adapting, and validating computer-vision and machine-learning methods for analyzing PV systems from satellite and aerial imagery.

Developing methods for detecting and characterizing changes in PV systems across imagery collected at different points in time.

Processing and analyzing large geospatial, image, and PV operational datasets.

Integrating image-derived information with PV performance, system metadata, permitting, and other complementary datasets.

Developing scalable computational workflows for application across large numbers of PV systems and images.

Evaluating algorithm accuracy, uncertainty, limitations, and generalizability using known-change and no-change systems.

Developing reproducible research software and data-analysis workflows using version control and collaborative software-development practices.

Applying statistical and machine-learning approaches to identify changes, anomalies, and trends in PV system…