{"id":2098109,"url":"https://alion.io/job/nlr-postdoctoral-researcher-vision-machine-learning","title":"Postdoctoral Researcher – Vision Machine Learning","company":{"id":1756652,"name":"NLR","domain":"nlr.gov","url":"https://alion.io/company/nlr-gov","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":91,"open_postings":6,"ghost_share":0,"stale_share":0.167,"repost_share":0,"time_to_fill_p50_days":61,"computed_at":"2026-10-12T06:55:09Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Golden, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":118000,"max_usd":258000,"period":"year","method":"role_country_seniority_unknown","sample_n":3786},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"GDAL","optional":false},{"name":"Image Segmentation","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenCV","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series Forecasting","optional":false}],"status":"live","first_seen_at":"2026-10-08T17:29:31Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-12T20:15:04Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Posting Title\nPostdoctoral Researcher - Vision Machine Learning.\nLocation\nCO - Golden.\nPosition Type\nPostdoc (Fixed Term).\nHours Per Week\n40.\nWorking at NLR\nNLR 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.\nAt 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.\nJob Description\nThe 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.\nA 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.\nThe 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.\nSpecific research activities may include:\nDeveloping, adapting, and validating computer-vision and machine-learning methods for analyzing PV systems from satellite and aerial imagery.\nDeveloping methods for detecting and characterizing changes in PV systems across imagery collected at different points in time.\nProcessing and analyzing large geospatial, image, and PV operational datasets.\nIntegrating image-derived information with PV performance, system metadata, permitting, and other complementary datasets.\nDeveloping scalable computational workflows for application across large numbers of PV systems and images.\nEvaluating algorithm accuracy, uncertainty, limitations, and generalizability using known-change and no-change systems.\nDeveloping reproducible research software and data-analysis workflows using version control and collaborative software-development practices.\nApplying statistical and machine-learning approaches to identify changes, anomalies, and trends in PV system performance.\nPublishing research results in peer-reviewed journals and presenting findings at technical conferences and project meetings.\nCollaborating with researchers across disciplines including PV performance and reliability, geospatial analysis, machine learning, materials, recycling, circularity and energy systems analysis.\n.\nBasic Qualifications\nMust be a recent PhD graduate within the last three years.* Must meet educational requirements prior to employment start date.\nAdditional Required Qualifications\nDemonstrated experience with computer vision and/or machine learning, particularly image segmentation, object detection, feature extraction, image registration, or change detection.\nExperience working with satellite, aerial, remote-sensing, or geospatial imagery.\nExperience developing or adapting algorithms for analysis of large image datasets.\nExperience with parallel computing, high-performance computing, distributed computing, and/or cloud-based computational workflows.\nExperience with large-scale time-series or operational datasets.\nExperience integrating heterogeneous datasets, such as imagery, geospatial information, operational measurements, and system metadata.\nExperience quantitatively validating computational methods, including assessment of algorithm accuracy, uncertainty, and limitations.\nExperience with relevant scientific-computing or machine-learning tools such as PyTorch, TensorFlow, OpenCV, scikit-learn, GDAL, Rasterio, GeoPandas, xarray, or similar tools.\nExperience with photovoltaic systems, renewable-energy performance data, or related energy-system applications.\nExperience developing open-source scientific software, reusable computational tools, or public research datasets.\nPreferred Qualifications\n.\nJob Application Submission Window\nThe anticipated closing window for application submission is up to 30 days and may be extended as needed.\nAnnual Salary Range (based on full-time 40 hours per week)\nJob Profile: Postdoctoral Researcher / Annual Salary Range: $76,600 - $126,400NLR takes into consideration a candidate’s education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee’s salary history will not be used in compensation decisions.\nBenefits Summary\nBenefits include 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; and paid holidays. NLR employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component. Some positions may be eligible for relocation expense reimbursement.* Based on eligibility rules\nBadging Requirement\nNLR is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as required by Homeland Security Presidential Directive 12 (HSPD-12), which includes a favorable background investigation.Drug Free Workplace\nNLR is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug.\nIf you are offered employment at NLR, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.\nSubmission Guidelines\nPlease note that in order to be considered an applicant for any position at NLR you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.\n.\nEqual Opportunity Employer\nAll qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.\nReasonable Accommodations\nE-Verifywww.dhs.gov/E-Verify For information about right to work, click here for English or here for Spanish.\nE-Verify is a registered trademark of the U.S. Department of Homeland Security. 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