Scientific Computing Researcher

SLB•Cambridge, MA

5d•$105,280 - $213,800•Onsite

About The Position

Schlumberger-Doll Research (SDR), SLB's global research and innovation center, is seeking a Scientific Computing Researcher to join a multidisciplinary team of scientists in Cambridge, Massachusetts, adjacent to the MIT campus. This is an exciting opportunity to contribute to breakthrough research with direct impact on some of the world's most pressing energy challenges. Working at the intersection of science, engineering, and advanced computing, you will help develop next-generation solutions that enable cleaner, more efficient, and more sustainable energy systems. The position is part of the Geosciences Department and focuses on advancing our capabilities in developing innovative computational solutions that enhance our understanding of geological systems, maximize the value of physical measurements, and improve operational efficiency in the characterization and management of subsurface assets. The successful candidate will collaborate closely with scientists, engineers, and domain experts to develop solutions combining physics-based modeling, inversion techniques, machine learning, and advanced data analytics to address critical industry challenges. As research priorities and technologies evolve, the scope of the role will expand to support emerging projects and strategic initiatives across SLB's energy portfolio.

Requirements

Master's degree or PhD in Applied Mathematics, Computational Science, Physics, Engineering, or a related quantitative discipline.
Strong foundation in applied mathematics, uncertainty quantification, statistical inference, signal processing, and data analysis.
Demonstrated experience applying mathematical or computational methods to solve problems in one or more physical science domains.
Proficiency in scientific programming using languages such as Python, C, or C++, along with associated scientific computing libraries.
Strong analytical thinking and problem-solving capabilities.
Ability to work effectively in a multicultural and multidisciplinary research environment.
Excellent written and verbal communication skills.

Nice To Haves

Experience with machine learning and artificial intelligence methods; exposure to agentic AI frameworks and applications is highly desirable.
Experience developing physics-informed or hybrid physics-AI solutions.
Familiarity with inverse problems, optimization methods, and uncertainty quantification techniques.
Experience working with geoscience, energy, or subsurface-related applications.
Track record of scientific publications, technical presentations, or applied research contributions.

Responsibilities

Partner with researchers, engineers, and domain experts to identify and develop technical solutions for real-world challenges.
Develop, test, and benchmark algorithms grounded in physical principles to interpret measurements and infer subsurface properties.
Assess the quality, reliability, and uncertainty of measurements and inversion-derived results.
Identify opportunities to improve estimation accuracy through the integration of multiple physics domains and data sources.
Design and develop prototype solutions for validation, deployment, and transfer to engineering teams.
Collaborate with multidisciplinary teams across geoscience, reservoir engineering, formation evaluation, data science, and software development.
Investigate and develop agentic AI solutions to assist data acquisition engineers in the planning and execution of field operations.
Publish research findings in leading scientific journals and present results at internal and external technical forums.
Support the transition of research innovations into practical applications and operational deployment.