Post Doctorate Research Associate - Computational Hydrology

Job Title

The successful candidate will contribute to research in subsurface flow and transport modeling, hydrogeology, and machine learning for environmental systems.

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 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.

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.

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…