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Reservoir Simulation Research Scientist Machine Learning & Optimization Focus

Reservoir Simulation Research Scientist Machine Learning & Optimization Focus

TachyusHouston, TX, US
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Job Description

Job Description

Salary : 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 CO 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.

    • remote work

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