Post Doctorate Research Associate - Computational Hydrology

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

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 [e-mail].

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 investigation post hire and receive a favorable federal adjudication. The tier of federal background investigation will be determined by job duties and national security or public trust responsibilities associated with the job. All tiers of investigation include a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last 1 to 7 years (depending on the applicable tier of investigation). Illegal drug activities include marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

For foreign national candidates:

If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) Federal risk determination to maintain employment. Once you meet the three‑year residency requirement thereafter, you will be required to obtain a PIV credential to maintain employment. The tier of federal background investigation required to obtain the PIV credential will be determined by job duties at the time you become eligible for the PIV credential.

Mandatory Requirements

Please be aware that the Department of Energy (DOE) prohibits DOE employees and contractors from having any affiliation with the foreign government of a country DOE has identified as a “country of risk” without explicit approval by DOE and Battelle. If you are offered a position at PNNL and currently have any affiliation with the government of one of these countries, you will be required to disclose this information and recuse yourself of that affiliation or receive approval from DOE and Battelle prior to your first day of employment.

Rockstar Rewards

• medical insurance
• dental insurance
• vision insurance
• robust telehealth care options
• several mental health benefits
• free wellness coaching
• health savings account
• flexible spending accounts
• basic life insurance
• disability insurance*
• employee assistance program
• business travel insurance
• tuition assistance
• relocation
• backup childcare
• legal benefits
• supplemental parental bonding leave
• surrogacy and adoption assistance
• fertility support
• Employees are automatically enrolled in our company-funded pension plan*
• may enroll in our 401 (k) savings plan with company match*
• Employees may accrue up to 120 vacation hours per year
• may receive ten paid holidays per year

Notice to Applicants

PNNL lists the full pay range for the position in the job posting. Starting pay is calculated from the minimum of the pay range and actual placement in the range is determined based on an individual’s relevant job‑related skills, qualifications, and experience. This approach is applicable to all positions, with the exception of positions governed by collective bargaining agreements and certain limited‑term positions which have specific pay rules.

As part of our commitment to fair compensation practices, we do not ask for or consider current or past salaries in making compensation offers at hire. Instead, our compensation offers are determined by the specific requirements of the position, prevailing market trends, applicable collective bargaining agreements, pay equity for the position type, and individual qualifications and skills relevant to the performance of the position.

Minimum Salary

USD $69,000.00/Yr.

Maximum Salary

USD $119,100.00/Yr.