Postdoc in Subsurface ML & Environmental Modeling

Pacific Northwest National Laboratory; Richland Township (OH); Hybrid; USD 69,000 - 119,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Flexible work options

Relocation assistance

Job summary

Pacific Northwest National Laboratory in Richland, WA, seeks a research scientist to advance subsurface flow and transport modeling, hydrogeology, and machine learning for environmental systems. You will develop and apply computational approaches for groundwater flow, multiphase transport, and reactive transport in porous and fractured media, and integrate data for calibration and forecasting.

The role emphasizes interdisciplinary collaboration, uncertainty quantification, and environmental

Qualifications

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

Responsibilities

• 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.

Skills

Subsurface flow modeling

Hydrogeology

Machine learning

Uncertainty quantification

Data integration

Education

PhD in Environmental Science

PhD in Geoscience

Tools

PFLOTRAN

STOMP

MODFLOW

MT3D

Job description

Pacific Northwest National Laboratory in Richland, WA, seeks a research scientist to advance subsurface flow and transport modeling, hydrogeology, and machine learning for environmental systems. You will develop and apply computational approaches for groundwater flow, multiphase transport, and reactive transport in porous and fractured media, and integrate data for calibration and forecasting.

The role emphasizes interdisciplinary collaboration, uncertainty quantification, and environmental