Data Scientist Phd Intern - Abingdon, United Kingdom - Schlumberger

Schlumberger

Verified Company

Abingdon, United Kingdom

2 weeks ago

Posted by:

Tom O´Connor

beBee Recruiter

Description

Job title:

Data Scientist (3 months)
Starting Summer 2024
Project: "Physics-Informed Machine Learning for Nonlinear Partial Differential Equations (PDEs) "
Location:
Abingdon, United Kingdom
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'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.

Description and Scope:

In this project, you will work on developing a physicsinformed 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 Netzero.
Deliverables
As part of the Numerical Simulation team, you will work on developing a physicsinformed machine learning model to solve Partial Differential Equations on general grids and geometries. You will have access to highfidelity 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 toptier 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.- 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.

-Data Scientist PhD Intern (3 months)