DuraMat Degradation Analysis Postdoctoral Fellow

About the role

Lawrence Berkeley National Laboratory is hiring a DuraMat Degradation Analysis Postdoctoral Fellow within the Energy Technologies and Systems division. The HackingMaterials group has confirmed funding to hire a postdoc within the DOE CMEI Durable Module Materials Program (DuraMat) to analyze degradation pathways of solar photovoltaic modules extracted from the scientific literature. The group has assembled a large literature-derived data set of reported module degradation modes, stressors, and outcomes; the data are heterogeneous, incomplete, and often contradictory.

You Will

• Analyze literature-extracted data on PV module degradation pathways, assessing data quality, consistency, and completeness
• Validate literature-derived conclusions against established degradation mechanisms (e.g., UV-induced degradation, damp heat, thermo-mechanical stress, delamination, metastability effects)
• Identify gaps and contradictions in reported degradation knowledge and formulate hypotheses that connect findings across studies
• Develop data analysis procedures (Python) to organize, filter, and cross-reference degradation reports across stressors, materials, and module architectures
• Collaborate with team members developing LLM-based literature extraction and agentic reasoning tools, providing domain feedback on extraction quality
• Prepare presentations and reports; write peer-reviewed journal articles

Additional Responsibilities As Needed

• Assist in outreach activities, workshops, and tutorials.

We Are Looking For

• Ph.D. in Chemistry, Materials Science, Physics, or a related field
• Deep knowledge of solar PV materials and their degradation mechanisms, including accelerated aging protocols and stressor–response relationships (UV, thermal, moisture, electrical bias, mechanical)
• Working knowledge of PV characterization techniques (e.g., electroluminescence/photoluminescence imaging, current–voltage measurement, thermography), including their sensitivities, artifacts, and limitations, sufficient to critically evaluate reported measurements in the literature
• Strong data analysis skills (Python and/or MATLAB) and statistical reasoning
• Excellent communication skills and demonstrated ability to work across multi-institutional collaborations

Desired Skills/knowledge

• Programmatic LLM usage or experience with LLM-extracted data sets
• Experience with IEC standards development or fielded-module reliability data
• Experience publishing in PV reliability venues (PVSC, Progress in Photovoltaics, Solar Energy Materials & Solar Cells)