| Location | College Park, Maryland |
| Hours | Temporary Full-Time |
| Applications deadline | 07/16/2026 |
Description
At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget.
Our Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus.
The Earth and Biological Sciences Directorate (EBSD) leads critical research in four areas: Atmospheric, Climate & Earth Sciences, Biological Sciences, Environmental Molecular Sciences, and Global Change. Our vision is to develop a predictive understanding of biological and Earth systems in transition. We aim to understand energy and material flows within the integrated Earth system; to understand, predict, and control the response of biosystems to environmental and/or genomic changes; and to Model the Earth system from the subsurface to the atmosphere.
The Global Change Division is home to the Joint Global Change Research Institute (JGCRI). JGCRI is a global leader in the interdisciplinary field of integrated assessment multi-sector analysis. Research at the institute is conducted to advance fundamental understanding of human and Earth systems and provide information related to global change, energy, and environment that is unbiased and decision-relevant but not policy prescriptive. The institute is a partnership between PNNL and the University of Maryland, supporting research, modeling, and integrated analysis at the interface of human, energy, and Earth systems.
Responsibilities
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.