Reservoir Simulation Research Scientist – Machine Learning

About the Role:

We are seeking a highly motivated Reservoir Simulation Research Scientist to contribute to the next generation of reservoir modeling technologies. This role focuses on the research and development (R&D) of advanced computational methods combining physics-based reservoir simulation with machine learning, data assimilation, and optimization. You will work on developing novel algorithms, enhancing simulation capabilities, and bridging data-driven and physics-based modeling approaches to support the energy transition and improve reservoir management workflows.

Key Responsibilities:

• Conduct fundamental and applied research in reservoir simulation, computational physics, and data-driven methods.
• Develop and prototype novel algorithms that integrate machine learning with traditional reservoir simulation workflows, including surrogate modeling, reduced-order modeling, and hybrid physics-ML models.
• Research and implement advanced data assimilation techniques, including ensemble-based methods, adjoint-based gradient optimization, and Bayesian inference for history matching and uncertainty quantification.
• Develop and apply optimization algorithms for field development planning, production enhancement, and reservoir control under uncertainty.
• Collaborate with cross-disciplinary teams including reservoir engineers, geoscientists, data scientists, and software engineers.
• Publish research outcomes in peer-reviewed journals, patents, and present at industry and academic conferences.
• Provide technical leadership in framing R&D roadmaps, identifying high-impact research directions, and supporting technology transfer into commercial or operational tools.
• Contribute to the development of internal software prototypes or production-grade software for reservoir modeling and AI-enabled workflows.

Required Qualifications:

• Ph.D. in Petroleum Engineering or Reservoir Engineering or a related field with a focus on numerical simulation, optimization, or machine learning applications.
• Strong background in numerical methods for PDEs, linear and nonlinear solvers, and reservoir flow physics.
• Expertise in reservoir simulation technologies, including finite difference, finite volume, or finite element methods applied to multiphase subsurface flow.
• Demonstrated research experience in one or more of the following:
  • Machine learning (e.g., surrogate modeling, neural networks, Gaussian processes, physics-informed ML)
  • Data assimilation (e.g., Ensemble Kalman Filter, Ensemble Smoother, Adjoint-based optimization, Bayesian inference)
  • Optimization (e.g., field development planning, well control optimization, robust optimization under uncertainty)
• Proficiency in scientific programming (ideally Python and MATLAB) for algorithm development and prototyping.
• Proven track record of peer-reviewed publications, conference presentations, or patents in relevant technical domains.

Preferred Qualifications:

• Experience integrating physics-based simulation with machine learning frameworks, including Physics-Informed Neural Networks (PINNs) or hybrid models.
• Knowledge of high-performance computing (HPC), parallel programming, or cloud computing for large-scale simulations.
• Familiarity with open-source or commercial reservoir simulators (e.g., MRST, Open Porous Media, Eclipse, Intersect, tNavigator, CMG).
• Experience with probabilistic modeling, uncertainty quantification, and decision-making under uncertainty.
• Background in related domains such as CO₂ sequestration, geothermal systems, or unconventional resources modeling is a plus.

Soft Skills:

• Strong analytical and problem-solving skills with a rigorous scientific approach.
• Ability to communicate complex technical ideas clearly to both technical and non-technical audiences.
• Self-driven, collaborative, and passionate about advancing the state of the art in reservoir engineering and computational sciences.
• Comfortable working in both independent research settings and collaborative, multi-disciplinary environments.

Why Join Us?

• Work on cutting-edge problems at the intersection of subsurface science, machine learning, optimization and computational physics.
• Be part of a collaborative R&D team influencing the future of energy, carbon management, and sustainable subsurface technologies.
• Opportunities to publish, patent, and contribute to open-source software or commercial products.
• Competitive compensation, research freedom, and professional growth in a dynamic, innovation-driven environment.