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National Laboratory of the Rockies

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Postdoctoral Researcher in Vision Machine Learning for Photovoltaic Fleet Analysis National Laboratory of the Rockies in United States

Degree Level

Postdoc

Field of study

Computer Science

Funding

The full-time annual salary range is $76,600–$126,400. Benefits include medical, dental, and vision insurance; short-term disability insurance, pension benefits, a 403(b) employee savings plan with employer match, life and accidental death and dismemberment insurance, paid time off, sick leave, and paid holidays, subject to eligibility rules. Some positions may be eligible for relocation expense reimbursement. Performance-, merit-, and achievement-based awards may be available but are not guaran

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Country

United States

University

National Laboratory of the Rockies

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Keywords

Computer Science
Environmental Science
Electrical Engineering
Remote Sensing
Energy Engineering
Computational Science
Computer Vision
Change Detection
Photovoltaic
Statistics
Geospatial Information
ML

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About this position

The National Laboratory of the Rockies (NLR) in Golden, Colorado, is seeking a Postdoctoral Researcher in Vision Machine Learning to work at the intersection of computer vision, machine learning, large-scale data analytics, and photovoltaic (PV) system performance.

The researcher will join a multidisciplinary team investigating how the U.S. photovoltaic fleet changes over time. A central focus is the development and application of computational methods to identify and characterize changes in utility-scale PV systems using satellite and aerial imagery, large operational datasets, and complementary geospatial and system-level information.

The project involves developing, adapting, and validating image-based identification and change-detection algorithms; processing large image, geospatial, and PV operational datasets; and integrating image-derived information with PV performance data, system metadata, permitting records, and other sources. The researcher will build scalable computational workflows for application across large numbers of PV systems and images, while evaluating algorithm accuracy, uncertainty, limitations, and generalizability.

The postdoctoral researcher will work closely with NLR’s PV performance and reliability research community and contribute to the PV Fleets research portfolio. Related research may address PV fleet evolution, repowering and decommissioning, end-of-service analysis, material-flow modeling, and PV circularity. The role also includes developing reproducible scientific software and data-analysis workflows using version control and collaborative software-development practices.

NLR provides a mission-driven research environment with state-of-the-art facilities, multidisciplinary teams, and collaborations spanning industry, academia, and national laboratories. The researcher will collaborate across PV performance and reliability, geospatial analysis, machine learning, materials, recycling, circularity, and energy systems analysis, with opportunities to publish in peer-reviewed journals and present findings at conferences and project meetings.

Funding details

The full-time annual salary range is $76,600–$126,400. Benefits include medical, dental, and vision insurance; short-term disability insurance, pension benefits, a 403(b) employee savings plan with employer match, life and accidental death and dismemberment insurance, paid time off, sick leave, and paid holidays, subject to eligibility rules. Some positions may be eligible for relocation expense reimbursement. Performance-, merit-, and achievement-based awards may be available but are not guaran

What's required

Applicants must be recent PhD graduates within the last three years and must meet educational requirements before the employment start date. Required experience includes computer vision and/or machine learning, especially image segmentation, object detection, feature extraction, image registration, or change detection; satellite, aerial, remote-sensing, or geospatial imagery; algorithms for large image datasets; parallel, high-performance, distributed, or cloud computing; large-scale time-series or operational datasets; heterogeneous dataset integration; quantitative validation of computational methods, including accuracy, uncertainty, and limitations; relevant tools such as PyTorch, TensorFlow, OpenCV, scikit-learn, GDAL, Rasterio, GeoPandas, or xarray; photovoltaic systems, renewable-energy performance data, or related energy-system applications; and open-source scientific software, reusable computational tools, or public research datasets. Candidates must be able to obtain and maintain a federal Personal Identity Verification card and satisfy Department of Energy access requirements, including a favorable background investigation. Employment is subject to work authorization, applicable DOE access rules, and a pre-employment drug test.

How to apply

Apply through the Workday application portal for requisition R14557. Submit a completed application form, cover letter, and resume; applications are required separately for each position.

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