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, 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?

Energy and Environment Directorate?delivers?science and technology solutions for the nation's biggest energy and environmental challenges. Our more than 1,700 staff support the Department of Energy (DOE), delivering on key DOE mission areas including: modernizing our nation's power grid to maintain a reliable, affordable, secure, and resilient electricity delivery infrastructure; research, development, validation, and effective utilization of renewable energy and efficiency technologies that improve the affordability, reliability, resiliency, and security of the American energy system; and resolving complex issues in nuclear science, energy, and environmental management. The?

Earth Systems Science Division, part of the Energy and Environment Directorate, provides leadership and solutions that advance Earth system opportunities for energy systems and national security. We are a multidisciplinary division connected by a shared commitment to innovate and collaborate towards solving complex problems in the dynamic Earth system. Responsibilities The successful candidate will contribute to research in subsurface flow and transport modeling, hydrogeology, and machine learning for environmental systems. The role will focus on developing and applying computational approaches for groundwater flow, contaminant fate and transport, and related subsurface processes. Research may include integration of model-generated and observational data, uncertainty quantification, inverse modeling, surrogate modeling, and optimization to support environmental remediation and decision support. The position will involve collaboration with interdisciplinary researchers across subsurface science, computational science, geoscience, and environmental management.

Responsibilities include:

Conduct research in subsurface flow and transport modeling, hydrogeology, and machine learning for environmental systems. Develop and apply computational approaches for groundwater flow, multiphase transport, and reactive transport in porous and fractured media, including PFLOTRAN/STOMP-based or related modeling workflows where appropriate. Integrate model-generated and observational data to support calibration, inversion, uncertainty quantification, and predictive analysis for subsurface systems. Contribute to the development of reduced-order, surrogate, and physics-informed machine learning methods for environmental and geoscience applications. Support research relevant to environmental remediation, including subsurface characterization, contaminant fate and transport, monitoring interpretation, and optimization-informed decision support. Collaborate with interdisciplinary researchers across subsurface science, computational science, geoscience, and environmental management. Publish results in peer-reviewed journals and present findings in technical meetings and conferences. Primarily office and computer-based research environment. May involve limited visits to laboratory, field, or site environments in support of project activities. Any field or site work would be conducted in accordance with applicable safety and training requirements. Although this position can be virtual, onsite presence at the PNNL campus in Richland, Washington is preferred.

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:

PhD in Environmental Science, Geoscience, Computational Hydrology/Hydrogeology, or a related field. Experience with machine learning, scientific machine learning, or physics-informed machine learning. Experience with groundwater flow, multiphase flow, reactive transport, or fractured/porous media modeling. Familiarity with PFLOTRAN/STOMP/MODFLOW/MT3D or similar subsurface simulation tools. Experience integrating observational and model-generated environmental data for calibration, validation, history matching, inversion, or forecasting. Experience in uncertainty quantification, inverse modeling, optimization, reduced-order modeling, or data assimilation. Familiarity with environmental remediation, contaminant transport, deep vadose zone or groundwater applications, and decision support for cleanup or monitoring strategy. Exposure to large language models or AI agents for scientific workflows is desirable. Testing Designated Position This is not a Testing Designated Position (TDP). 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. PNNL is located in eastern Washington State-the dry side of Washington known for its stellar outdoor recreation and affordable cost of living. The Lab's campus is only a 45-minute flight (or ~3 hour drive) from Seattle or Portland, and is serviced by the convenient PSC airport, connected to 8 major hubs. 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 . Drug Free Workplace PNNL is committed to a drug-free workplace supported by Workplace Substance Abuse Program (WSAP) and complies with federal laws prohibiting the possession and use of illegal drugs. If you are offered employment at PNNL, you must pass a drug test prior to commencing employment. PNNL complies with federal law regarding illegal drug use. Under federal law, marijuana remains an illegal drug. If you test positive for any illegal controlled substance, including marijuana, your offer of employment will be withdrawn. Security, Credentialing, and Eligibility Requirements As a national laboratory, PNNL is responsible for adhering to the Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which require new employees to obtain and maintain a HSPD-12 Personal Identify Verification (PIV) Credential. To obtain this credential, new employees must successfully complete the applicable tier of federal background investigatio