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DuraMat Research Scientist

Job in Berkeley, Alameda County, California, 94720, USA
Listing for: Lawrence Berkeley National Laboratory
Full Time position
Listed on 2026-07-18
Job specializations:
  • Research/Development
    Research Scientist, Data Scientist
Job Description & How to Apply Below

Dura Mat Research Scientist

The Career-Track Research Scientist in the Energy Technologies and Systems Division will conduct research on solar photovoltaic (PV) module degradation within the DOE CMEI Dura Mat Program and lead the development of the group's open-source PV degradation analysis tools. This role includes developing physics-based and statistical methods for PV degradation analysis, building agentic AI systems for autonomous PV and materials data analysis, conducting independent research, contributing to funding proposals, and providing scientific leadership to junior staff.

You will be eligible for PI status upon completing required training. We're here for the same mission, to bring science solutions to the world. Join our team and YOU will play a supporting role in our goal to address global challenges! Have a high level of impact and work for an organization associated with 17 Nobel Prizes!

Why join Berkeley Lab? We invest in our employees by offering a total rewards package you can count on:
Exceptional health and retirement benefits, including pension or 401K-style plans. A culture where you'll belong - we are invested in our teams! In addition to accruing vacation and sick time, we also have a Winter Holiday Shutdown every year. Parental bonding leave (for both mothers and fathers) Pet insurance

You will:

  • Develop innovative solutions to complex PV degradation challenges, leading independent projects and contributing to multi-institutional research efforts.
  • Lead the development and technical direction of the group's open-source PV analysis tools (PVPRO, Vocmax) and the pvtools.lbl.gov platform, including new physics-based and data-driven degradation models.
  • Develop and apply physics-based, statistical, and machine learning methods to analyze PV degradation from operational and I–V data.
  • Build agentic AI systems and Python-based tools for autonomous PV and materials data analysis.
  • Conduct research within the Dura Mat program, progressing to an independent research program by the Mid-Term Evaluation.
  • Publish peer-reviewed research, progressing to lead-author publications by the Mid-Term Evaluation.
  • Contribute to funding proposals and submit proposals by the Mid-Term Evaluation.
  • Present research at conferences and seminars, and report progress to DOE program managers.
  • Supervise and mentor junior staff and students.
  • Build and maintain collaborations across the research community.

Additional Responsibilities as needed:
Act as liaison between PIs / Scientists, other LBNL employees, and external contacts.

We are looking for:

  • Advanced degree in Electrical Engineering, Materials Science, Physics, or a related field, and 3–5 years of relevant professional experience.
  • Excellent academic record and evaluations
  • Demonstrated experience applying advanced principles, theories, and concepts to R&D problems in solar PV analysis and degradation
  • Demonstrated experience developing and maintaining open-source scientific software
  • Significant experience programming in Python on both frontend (e.g., Plotly Dash) and backend (e.g., numpy, scipy)
  • Demonstrated experience preparing funding proposals, and independently planning and completing projects
  • Proven record of publications and achievements, including lead-author publications on solar PV module degradation analysis (e.g., circuit-model parameter extraction, I–V curve correction, degraded-system power modeling)
  • Ability to apply advanced principles, theories, and concepts to R&D problems in PV degradation analysis, and to conduct creative research progressing toward independent direction
  • Proficiency in Python (frontend and backend) and scientific computing
  • General knowledge of solar photovoltaics, module degradation mechanisms, and PV performance analysis (pvlib, rdtools)
  • Prior experience with statistical data analysis and/or machine learning applied to scientific data
  • Programmatic LLM usage within agentic frameworks
  • Ability to collaborate effectively with a multidisciplinary team of scientists, industry partners, and international collaborators
  • Excellent verbal, written, and presentation skills
  • Ability to work effectively both independently and in a team environment
  • Excellent…
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