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We are a global technology company, driving energy innovation for a balanced planet.

At SLB we create amazing technology that unlocks access to energy for the benefit of all. That is our purpose. As innovators, that’s been our mission for 100 years. We are facing the world’s greatest balancing act- how to simultaneously reduce emissions and meet the world’s growing energy demands. We’re working on that answer. Every day, a step closer.

Our collective future depends on decarbonizing the fossil fuel industry, while innovating a new energy landscape. It’s what drives us. Ensuring progress for people and the planet, on the journey to net zero and beyond. For a balanced planet.

The role

​​​​​Numerical simulation remains the only reliable method to solve partial differential equations to predict future states of a complex physical system - be it weather, fluid flow, quantum dynamics or orbital mechanics. SLB’s state-of-the-art reservoir simulator is used to model such a fluid flow in porous media for various applications, including Carbon Capture and Storage (CCS) and geothermal energy systems. The drawback of traditional numerical methods, however, is that they are computational very intensive and are not practical for many realistic workflows.

In this project, you will work on developing a physics-informed machine learning model to predict how a reservoir system behaves when CO2 is injected into it. Machine Learning models have been shown to run orders of magnitude faster than conventional simulators and, once trained, provide a promising alternative or enhancement to traditional solvers. The ultimate goal is to use the developed machine learning model to find optimal locations and volumes of CO2 to inject into a subsurface in order to maximize carbon storage and accelerate the global push towards Net-zero.​


​As part of the Numerical Simulation team, you will work on developing a physics-informed machine learning model to solve Partial Differential Equations on general grids and geometries. You will have access to high-fidelity 3D simulator data to develop and train novel Neural Operator and Graph Neural Network architectures. You will also be integrating this model into full workflows to show that ML solutions run orders of magnitude faster than traditional methods and will have the opportunity to publish in top-tier ML and Applied Mathematics conferences/journals (ICML, NeurIPs, ICLR etc.)

​Required Skills & Qualifications

  • ​PhD student in​ ​Machine Learning, Mathematics, Physics or​ a related discipline
  • Oral and written communication skills in English
  • Good motivation, autonomy, teamwork, and ingenuity
  • Theoretical and practical knowledge in machine learning and deep learning
  • Strong programming skills and experience with one of Scikit/PyTorch/Tensorflow etc.
  • Ability to quickly understand and implement models from research papers

Experience in one of the following would be advantageous

  • Neural Operators, DeepONets or Graph Neural Networks etc
  • Knowledge of mass conservation equations and material balance
  • Simulation basics (numerical methods for solving PDE systems)
  • Previous research experience in the form of internships, academic theses and/or publications ​

BlueFlex: We are open to flexible, hybrid working with a combination of on-site & home working days.