Subsurface Reactive Fate & Transport Modeling Scientist

Savannah River National Laboratory (SRNL) is seeking a midcareer scientist with strong expertise in subsurface reactive fate and transport modeling to support research and mission-driven applications focused on environmental remediation, groundwater protection, and long-term stewardship at the Savannah River Site (SRS) and other DOE-managed lands. 

The successful candidate will develop, apply, and communicate advanced numerical models to predict the coupled hydrologic, geochemical, and biological processes governing contaminant behavior in complex subsurface environments. This role supports SRNL’s mission to deliver credible science-based solutions that ensure the protection of human health and the environment.

The position involves technical leadership, collaboration across multidisciplinary teams, mentoring of early-career staff, and direct interaction with DOE customers and regulatory stakeholders.

Technical Modeling & Analysis

• Develop and apply reactive transport models (e.g., PFLOTRAN, TOUGHREACT, CrunchFlow/CrunchTope, PHREEQC, HYDRUS, COMSOL, FEFLOW) to evaluate contaminant fate, plume evolution, and remedial strategy performance. 
• Integrate aqueous geochemistry, sorption/kinetics, mineral reactions, and redox transformations into model frameworks. 
• Conduct model calibration, validation, sensitivity analysis, and uncertainty quantification using tools such as PEST/PEST++, DAKOTA, or Bayesian methods. 
• Develop and refine conceptual site models using hydrogeologic, geochemical, mineralogical, and field performance data. 

Data Integration & Interpretation

• Assimilate field monitoring, laboratory testing, geospatial data, and historical site knowledge into modeling workflows. 
• Generate reduced-order models, computational surrogates, or decision-support tools to inform risk assessments and remediation planning. 
• Produce high-quality visualizations, technical interpretations, and defensible documentation suitable for DOE and regulatory review.

Program and Project Support

• Serve as a technical lead or task manager for modeling efforts within multi-institutional or multi-disciplinary projects. 
• Contribute to proposal development, R&D planning, and engagement with DOE program managers, site operations personnel, and regulators. 
• Ensure all deliverables meet SRNL’s standards for quality, safety, and regulatory compliance.

Collaboration & Mentorship

• Mentor junior scientists, modelers, and interns on modeling techniques, scientific writing, and workflow reproducibility. 
• Work closely with SRNL hydrologists, geochemists, microbial scientists, field engineers, and data scientists to design studies and integrate models with experimental work.

Competencies

• Strong technical judgment and ability to design rigorous, defensible modeling studies. 
• Effective communicator, able to translate complex technical outputs into clear, actionable insights. 
• Demonstrated ability to work effectively in multidisciplinary teams and build strong collaborations across SRNL, SRS operations, and partner organizations. 
• Commitment to safety, quality, and scientific integrity. 

Minimum Qualifications:

• Ph.D. in Hydrogeology, Geosciences, Environmental Engineering, Chemical Engineering, Civil Engineering, or related field with 6-8+ years of relevant experience; or M.S. with 8-10 years of relevant experience. 
• Ability to obtain and maintain security clearance; U.S. citizenship is legally required. 
• Demonstrated experience developing and calibrating reactive fate and transport models for groundwater or vadose-zone systems. 
• Strong foundation in aqueous geochemistry and subsurface flow and transport processes. 
• Proficiency in Python (preferred), MATLAB, or R for model pre- and post-processing. 

• Experience preparing technical reports and communicating results to technical and non-technical audiences. 

   

Preferred Qualifications:

• Experience with HPC environments, parallel computing, containers, or cloud-ready workflows. 
• Background in geostatistics, stochastic modeling, or Bayesian methods. 
• Experience with contaminants relevant to DOE legacy sites (e.g., uranium, technetium, strontium, cesium, nitrate, chlorinated solvents, PFAS). 
• Familiarity with DOE Orders, CERCLA, and other environmental regulatory frameworks. 
• Experience with reduced-order modeling, machine learning integration, or physics-informed ML methods. 
• Experience supporting or leading DOE-funded R&D programs. 

• Knowledge of GIS tools (ArcGIS/QGIS), geospatial analysis, and database management