Data Scientist Intern (3 months)

Job Title:

Data Scientist Intern (3 months) - Starting Summer 2026

Project Title: ​​Inverse problems using physics informed neural proxy models ​

About SLB:

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 has 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.

Our purpose: Together, we create amazing technology that unlocks access to energy for the benefit of all. You can find out more about us on

Location:

Abingdon, Oxfordshire

Description & Scope:

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 (or any other fluid) is injected into it. Machine Learning models have provably 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 and embed these in complex field development planning workflows. You will work on ensemble optimization and inverse problems. ​

Responsibilities

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

Qualifications:

​​Studying a PhD​ in ​Applied Mathematics, Applied Physics, Data Science or a related discipline ​​Strong mathematical concepts around Optimization and Inverse theory Partial Differential Equations Python PyTorch/Tensorflow​