Lawrence Berkeley National Lab’s (LBNL) Energy Geosciences Division has an opening for a Data Scientist to join the team.

In this role, you will be primarily responsible for the development, implementation, and testing of a wide range of numerical and machine learning (ML) computer algorithms as applied to reservoir engineering and geophysical imaging. This includes the simulation of thermal-hydro-mechanical-chemical (THMC) processes in complicated subsurface structures and the application of AI/ML-based approaches to better understand petroleum and geothermal reservoirs, and critically, Geologic Carbon Storage (GCS) applications. You will specifically apply these methods to dynamic reservoir modeling, area of review evaluation, and wellbore integrity assessment of GCS in both new fields and reuse scenarios. You will also engage in AI/ML-based inverse modeling for the analysis of well test and fluid-flow experimental data, the development and deployment of AI/ML models into software tools and programs, and performance assessment for nuclear waste disposal.

What You Will Do:

Design, implement, test, and deploy software solutions for reservoir engineering/modeling and geophysical imaging using machine and statistical learning techniques.
Support scientific staff in development for reservoir modeling algorithms for petroleum, geothermal, and geologic carbon storage applications. Some familiarity with LBNL’s TOUGH suite of modeling codes is preferred.
Support scientific staff and develop software methods to effectively use HPC resources (e.g., Laurencium and NERSC)
Apply visualization and analysis software packages to facilitate software development.
Share results in group and department meetings.
Contribute to peer-reviewed journals and funding proposals.

What is Required:

Master’s degree in the physical or computer sciences with at least six years of experience in developing programming solutions for reservoir engineering, or PhD with at least three years of experience in this area, or equivalent work experience.
Experience in contributing to peer-reviewed journal articles and funding proposals.
Experience working in high-performance computing environments, including on both CPU and GPU clusters.
Experience in implementing, testing, and demonstrating algorithms and workflows involving both linear and non-linear processes.
Experience implementing machine and statistical learning algorithms; knowledge of federated learning environments and tools such as TensorFlow is a plus.
Demonstrated experience in geophysical modeling, inversion, and imaging workflows, with emphasis on machine and statistical learning methodologies.
Strong time management, organization, and multitasking skills, with the ability to meet deadlines.
Ability to quickly learn and apply new computational concepts, programming languages, and software libraries to reservoir engineering and geophysical problems.
Ability to work effectively in a diverse, collaborative team environment.

Desired Qualifications:

Demonstrated ability to work with geo-scientists from diverse backgrounds.

Notes:

This is a full-time, career appointment, exempt (monthly paid) from overtime pay.
The full salary range of this position is between $139,440 to $235,308 per year and is expected to pay between a targeted range of $156,864 to $191,724 per year depending upon candidates' full skills, knowledge, and abilities, including education, certifications, and years of experience.
This position is subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
Work will be primarily performed at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. A REAL ID or other acceptable form of identification is required to access Berkeley Lab sites.

Want to learn more about working at Berkeley Lab? Please visit: careers.lbl.gov

Equal Employment Opportunity Employer: The foundation of Berkeley Lab is our Stewardship Values: Team Science, Service, Trust, Innovation, and Respect; and we strive to build community with these shared values and commitments. Berkeley Lab is an Equal Opportunity Employer. We heartily welcome applications from all who could contribute to the Lab's mission of leading scientific discovery, excellence, and professionalism. In support of our rich global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories under State and Federal law.

Berkeley Lab is a University of California employer. It is the policy of the University of California to undertake affirmative action and anti-discrimination efforts, consistent with its obligations as a Federal and State contractor.

Misconduct Disclosure Requirement: As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct, are currently being investigated for misconduct, left a position during an investigation for alleged misconduct, or have filed an appeal with a previous employer.

Lawrence Berkeley National Laboratory

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