Overview

PNNL's Earth System Science Division enables energy independence and national security through leadership in earth systems science, engineering, and decision analytics. Our work focuses on solving complex problems in the dynamic Earth system. Our interdisciplinary scientists steward a breadth of efforts that encompass research on plants to groundwater science and coastal zones, to storm prediction.

Our research focuses on understanding and mitigating operational risks at the interface of human and natural environments. This includes predicting the impacts of natural hazards and extreme climate events on Earth and human systems, along with the impacts of wildfire, flooding, sea level rise and storm surges. We focus on understanding and mitigating environmental contamination and increasing the resiliency, security and sustainability of water resources. We provide geointelligence through advanced sensing and data analytics to forecast complex system behaviors and operational performance to understand human-natural systems. This includes informed decision making and enhanced community resilience, advanced monitoring, and remote sensing of environmental systems for energy and national security. It also includes developing energy systems, including geothermal energy, sustainable oil and gas production, storage and utilization, along with carbon sequestration.

Driven by a “science-to-solutions” philosophy, we provide scientific leadership and technology to enhance national security, mitigate natural hazards and optimize disaster response. In the critical areas of energy, environment, intelligence, and defense, we deliver insights and decision support through the development of tools and solutions.

Responsibilities

The Computational Geophysics Team, within the Subsurface Science Group, is seeking applications for a talented, highly motivated post-doctoral researcher to join its team. The postdoctoral researcher will primarily focus on the development and implementation of Artificial Intelligence (AI) and deep learning-based methods for the processing and inversion of geophysical data. This role involves creating innovative solutions to enable real-time inversion and classification of geophysical data, aiming to significantly reduce computational times and enhance decision-making in near real-time scenarios. The postdoc will work on leveraging advanced deep learning architectures, such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Autoencoders, and Conditional Variational Autoencoders (CVAE) to develop algorithms capable of instantaneously analyzing data and producing images of subsurface properties. The candidate will engage in the full cycle of algorithm development—from theoretical inception and numerical simulation to practical implementation and validation using real-world data. This position also requires active collaboration with internal teams and external partners to integrate multiple types of geophysical data, improving the accuracy and reliability of subsurface models. Additionally, the researcher will contribute to the refinement and enhancement of these models by incorporating site-specific data, thereby improving the prediction capabilities of the system. The ultimate goal is to execute these algorithms via edge computing, enabling efficient on-site data analysis even in environments with limited connectivity.

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 Geophysics, Physics, Applied Mathematics, Computational Science, or a related field.

• Experience in modeling and inversion of geophysical data, including electromagnetic, magnetic, gravity, seismic, and/or ground penetrating radar data.

• Proficiency in numerical methods, machine learning, deep learning, and scientific programming, including Python.

• Proficiency in deep learning libraries such as TensorFlow, PyTorch, and/or JAX.

• Working knowledge of deep learning architectures such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Autoencoders, Conditional Variational Autoencoders (CVAE), and/or hybrid architectures.

• Experience developing or working with software that utilizes continuous integration tools (e.g., distributed version control, automated testing).

• A record of peer-reviewed publications, active engagement in professional societies, and presentations at professional conferences.

Hazardous Working Conditions/Environment

Not Applicable.

Additional Information

Not Applicable.

Testing Designated Position

Not a Testing Designated Position.

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, flexible work schedules and telework options. 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, Diversity, Equity, Inclusion, and Equal Employment Opportunity

Our laboratory is committed to a diverse and inclusive work environment dedicated to solving critical challenges in fundamental sciences, national security, and energy resiliency. We are proud to be an Equal Employment Opportunity and Affirmative Action employer. In support of this commitment, we encourage people of all racial/ethnic identities, women, veterans, and individuals with disabilities to apply for employment.

Pacific Northwest National Laboratory considers all applicants for employment without regard to race, religion, color, sex (including pregnancy, sexual orientation, and gender identity), 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.

HSPD-12 PIV Credential Requirement

In accordance with Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, new employees are required to obtain and maintain a HSPD-12 Personal Identity Verification (PIV) Credential. To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

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

Employees and their families are offered medical insurance, dental insurance, vision insurance, health savings account, flexible spending accounts, basic life insurance, disability insurance, employee assistance program, business travel insurance, tuition assistance, supplemental parental bonding leave, surrogacy and adoption assistance, and fertility support. Employees are automatically enrolled in our company funded pension plan and may enroll in our 401k savings plan. Employees may accrue up to 120 vacation hours per year and may receive ten paid holidays per year.

• Research Associates excluded.

**Once eligibility requirements are met.

Click Here For Rockstar Rewards (https://careers.pnnl.gov/rockstar-rewards)

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.