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- Tasks: Develop and test innovative AI solutions for energy simulation challenges.
- Company: Join SLB, a global leader in energy technology and innovation.
- Benefits: Gain hands-on experience, mentorship, and potential publication opportunities.
- Why this job: Make a real impact on energy solutions while advancing your skills in AI.
- Qualifications: Pursuing a Master's in AI, Software Development, or related fields with coding skills.
- Other info: Inclusive workplace with a focus on innovation and career growth.
Job Description
AI Engineer Intern (3 or 6 months) – Starting Summer 2026
Project Title: Application of a cell-based machine learning model as a non-linear solver pre-conditione r
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 company website
Location:
Abingdon, Oxfordshire
Description & Scope:
In reservoir simulation, a system of non-linear equations is discretized and solved implicitly using the Newton-Raphson method. To enhance solver performance and convergence, various strategies are employed, including inexact Newton methods, preconditioning techniques, multiscale solvers, and others.
This internship explores the integration of a Neural Network (NN) single-cell method as a preconditioner for the non-linear system. The approach involves collecting simulation runtime data for each grid cell and feeding it into a pre-trained neural network to generate improved initial guesses prior to invoking the non-linear solver.
This technique is inspired by the methodology presented in "Learning to Solve Parameterized Single-Cell Problems Offline to Expedite Reservoir Simulation" by Abdul-Akeem Olawoyin and Rami Younis (SPE-212175-MS, 2023).
Responsibilities
The intern will work closely with the Intersect innovation team to implement and test the pre-conditioner inside Intersect code. By the end of the internship the following technical deliverables are expected:
- Coupling pre-trained neural-network models with an existent commercial simulator to create a pre-conditioner for the non-linear solvers
- Testing different neural-network architectures to identify how to best solve this problem
- Propose an automatic method to export simulation data in a structured format, to be used as training data for the pre-conditioner
- This an innovation work, with an opportunity to publish it in a research journal.
Qualifications :
- Studying a Masters in Software development, Artificial Intelligence, Neural Networks, Machine Learning, Numerical methods or a related discipline
- Python and C++ coding languages, neural-network model and training, numerical methods for solving ODEs and PDEs.
SLB is an equal employment opportunity employer. Qualified applicants are considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or other characteristics protected by law.
The recruiting process and the position can be adapted to fit most disabilities, please do not hesitate to mention this when applying.
AI Engineer Intern (3 or 6 months) employer: Schlumberger
Contact Detail:
Schlumberger Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer Intern (3 or 6 months)
✨Tip Number 1
Network like a pro! Reach out to current or former employees at SLB on LinkedIn. A friendly chat can give us insider info and maybe even a referral, which can really boost our chances.
✨Tip Number 2
Prepare for the interview by brushing up on your technical skills. Since this role involves Python and C++, let’s make sure we can talk confidently about our projects and how they relate to the job description.
✨Tip Number 3
Show off our passion for energy innovation! During interviews, let’s share our thoughts on decarbonisation and how AI can play a role in it. This will show that we’re aligned with SLB’s mission.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure our application gets seen by the right people. Plus, we can keep track of our application status easily.
We think you need these skills to ace AI Engineer Intern (3 or 6 months)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the AI Engineer Intern role. Highlight relevant skills like Python, C++, and any experience with neural networks or machine learning. We want to see how your background fits with our innovative projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for energy innovation and how you can contribute to our mission. Be sure to mention any specific projects or experiences that relate to the internship.
Showcase Your Projects: If you've worked on any relevant projects, whether in school or on your own, make sure to include them. We love seeing practical applications of your skills, especially if they relate to neural networks or numerical methods!
Apply Through Our Website: Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team!
How to prepare for a job interview at Schlumberger
✨Know Your Stuff
Make sure you brush up on your knowledge of neural networks, machine learning, and numerical methods. Be ready to discuss how these concepts apply to the role, especially in relation to the Newton-Raphson method and preconditioning techniques.
✨Show Your Passion for Innovation
SLB is all about driving energy innovation, so demonstrate your enthusiasm for creating technology that benefits the planet. Share any personal projects or experiences that highlight your innovative thinking and problem-solving skills.
✨Prepare Questions
Have a few thoughtful questions ready to ask your interviewers. This could be about their current projects, the team dynamics, or how they envision the future of energy technology. It shows you're genuinely interested and engaged.
✨Practice Coding Challenges
Since coding is a big part of this role, practice some Python and C++ coding challenges beforehand. Be prepared to explain your thought process and approach to solving problems during the interview, as this will showcase your technical skills.
