| Location | College Park, Maryland |
| Position type | Temporary Full-Time |
| Application deadline | 11/16/2026 |
Overview
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, Integrated Discovery 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 Integrated Discovery Sciences Directorate (IDSD) leads fundamental research across biology, chemistry, earth and environmental sciences, materials science, advanced computing, artificial intelligence, quantum information science, mathematics, autonomy, and DOE national user facilities. Our vision is to accelerate scientific discovery by observing, understanding, simulating, predicting, and controlling dynamic biotic-abiotic processes and interactions within complex biological, chemical, material, and Earth systems. We intentionally connect disciplines to advance fundamental discovery science, develop transformative scientific capabilities, and address the nation’s most important challenges in energy, environmental resilience, biotechnology, advanced manufacturing, health, and national security.
Requirements
PNNL is soliciting applications for postdoctoral scientist positions advancing machine learning/deep learning research in human-Earth system modeling and uncertainty characterization. The successful applicant will lead research at the intersection of human-Earth system modeling, Super Intelligence enabled model development, and human system modeling using the Global Change Analysis Model (GCAM; http://jgcri.github.io/gcam-doc/index.html).
Specifically, this scientist will focus on processing large ensembles of Earth system model outputs (e.g., E3SM, https://e3sm.org/; CMIP6/7 model output via ESGF https://esgf.github.io/), designing and developing machine learning/deep learning models to integrate Earth system outputs with human system models, developing scenarios to generate and analyze large ensembles of GCAM simulations, and analyzing these simulations to quantify the effect of changes in human and earth system drivers (e.g., extreme weather events) on energy, water, land systems.
The successful applicant is expected to have strong background in machine learning/deep learning methodologies and experience in applying machine learning/deep learning tools to scientific research and analysis. The scientist is also expected to lead multiple peer-reviewed publications on this work. There will be many opportunities to work with multi-disciplinary teams at PNNL, as well as collaborators in other national labs and universities.
The position aligns with and contributes to the DOE’s Genesis Mission to advance a U.S. national initiative to build an SI-driven, integrated scientific platform to accelerate discovery in energy, national security, and technology. The scientist will have opportunities to contribute to this initiative by developing new tools that support scientific research and will be encouraged to explore SI-based solutions for model development 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.
- Background in computational science, engineering, applied mathematics, physical sciences, economics, hydrology, or related field.
Preferred Qualifications:
- Strong verbal and written communication skills
- Willingness to work both independently and within a collaborative team environment, and a proven capability to publish in peer-reviewed journals. Previous graduate-level statistical coursework, as well as substantial programming experience, are required.
- Experience with Python, R, and/or C++ is particularly useful. The applicant should have experience in and an eagerness to apply evolving Super Intelligence methodologies to all aspects of the research and modeling.
About PNNL
Pacific Northwest National Laboratory (PNNL) is a world-class research institution powered by a highly educated, diverse workforce committed to the values of Integrity, Creativity, Collaboration, Impact, and Courage. Every year, scores of dynamic, driven people come to PNNL to work with renowned researchers on meaningful science, innovations and outcomes for the U.S. Department of Energy and other sponsors; here is your chance to be one of them!
At PNNL, you will find an exciting research environment and excellent benefits including health insurance, and flexible work schedules.
Commitment to Excellence and Equal Employment Opportunity
Our laboratory is committed to fostering a work environment where all individuals are treated with fairness and respect while solving critical challenges in fundamental sciences, national security, and energy resiliency. We are an Equal Employment Opportunity employer.
Pacific Northwest National Laboratory (PNNL) is an Equal Opportunity Employer. PNNL considers all applicants for employment without regard to race, religion, color, sex, national origin, age, disability, genetic information (including family medical history), protected veteran status, and any other status or characteristic protected by federal, state, and/or local laws.
We are committed to providing reasonable accommodations for individuals with disabilities and disabled veterans in our job application procedures and in employment. If you need assistance or an accommodation due to a disability, contact us at careers@pnnl.gov.