Post Doctorate Research Associate – Earth Systems Sciences at PNNL

PNNL is soliciting applications for postdoctoral scientist positions advancing research and modeling of energy, critical minerals and materials (CMM), and supply chain systems. This research will contribute to the Global Change Intersectoral Modeling System (GCIMS) Science Focus Area led by PNNL and sponsored by DOE BER, as well as other projects at PNNL. The successful applicant will lead the analysis using the Global Change Analysis Model (GCAM; http://jgcri.github.io/gcam-doc/index.html). Specifically, this scientist will focus on model, data, and scenario development to generate GCAM simulations that quantify the impacts of uncertain technologies, resources, and other relevant factors on the integrated energy, CMM, and economic systems. The scientist is expected to apply advancing artificial intelligence and machine learning tools in this analysis where appropriate. The scientist is also expected to lead multiple peer-reviewed publications on this work; there will be many opportunities to work with the multi-disciplinary PNNL team as well as collaborators in other national labs and universities.

PNNL’s GCIMS scientific focus area seeks to improve the understanding of the complex interactions among energy, CMMs, water, land, Earth systems, socioeconomics, and other important human and natural systems at regional to global and near term to decadal scales. GCIMS is also aligned with supporting and contributing to the DOE’s Genesis Mission for advancing AI. The GCIMS project develops and uses the GCAM model along with a suite of dedicated, open-source systems models. The scientist will have opportunities to contribute to developing new GCAM capabilities and tools for assisting the research and will be encouraged to develop AI solutions consistent with DOE’s Genesis Mission.

Qualifications
Minimum Qualifications:

• Candidates must have received a PhD within the past five years (60 months) or within the next 8 months from an accredited college or university.

Preferred Qualifications:

• Ph.D. in engineering, economics, public policy, data science, physical sciences, applied mathematics, computational science, or a related discipline.
• Strong verbal and written communication skills.
• Demonstrated ability to work independently as well as collaboratively within a team environment.
• Proven record of publishing in peer‑reviewed journals.
• Graduate-level statistical training and substantial programming experience.
• Proficiency with R, Python, and/or C++ is highly valuable.
• Experience with, and enthusiasm for, applying evolving AI methodologies to research, modeling, and analysis.

Full announcement available here.

Application deadline: 16 July 2026, 4.00 P.M. PST