Postdoctoral Researcher – Vision Machine Learning
About The Position
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.
Requirements
- Must be a recent PhD graduate within the last three years.
- Must meet educational requirements prior to employment start date.
- Demonstrated experience with computer vision and/or machine learning, particularly image segmentation, object detection, feature extraction, image registration, or change detection.
- Experience working with satellite, aerial, remote-sensing, or geospatial imagery.
- Experience developing or adapting algorithms for analysis of large image datasets.
- Experience with parallel computing, high-performance computing, distributed computing, and/or cloud-based computational workflows.
- Experience with large-scale time-series or operational datasets.
- Experience integrating heterogeneous datasets, such as imagery, geospatial information, operational measurements, and system metadata.
- Experience quantitatively validating computational methods, including assessment of algorithm accuracy, uncertainty, and limitations.
- Experience with relevant scientific-computing or machine-learning tools such as PyTorch, TensorFlow, OpenCV, scikit-learn, GDAL, Rasterio, GeoPandas, xarray, or similar tools.
- Experience with photovoltaic systems, renewable-energy performance data, or related energy-system applications.
- Experience developing open-source scientific software, reusable computational tools, or public research datasets.
Responsibilities
- 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 performance.
- Publishing research results in peer-reviewed journals and presenting findings at technical conferences and project meetings.
- Collaborating with researchers across disciplines including PV performance and reliability, geospatial analysis, machine learning, materials, recycling, circularity and energy systems analysis.
Benefits
- medical, dental, and vision insurance
- short-term disability insurance
- pension benefits
- 403(b) Employee Savings Plan with employer match
- life and accidental death and dismemberment (AD&D) insurance
- personal time off (PTO) and sick leave
- paid holidays
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
