Machine Learning Engineer Intern (3-6 Months) - Physics Informed Machine Learning for Geochemical Simulations

☞ SLB

View: 102

Update day: 01-06-2024

Location: Abingdon South East

Category: High Technology Mechanical / Technical Electrical / Electronics IT - Software

Industry: Oil Gas

Job type: Full-time

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Job content

Job Title

Machine Learning Engineer Intern (3-6 Months) - Physics Informed Machine Learning for Geochemical Simulations

About Us

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.

Location:

Abingdon, UK

Job Summary

Numerical simulation remains the only reliable method to predict the future state of a system - be it weather, fluid flow, landing on Mars or physics powered realistic gaming. A reservoir simulator is used to model fluid flow in porous media for various applications including carbon capture and storage and geothermal energy systems. The drawback of these methods is that they are computationally extremely slow and hence not practical for many realistic workflows. In this project, you will work on developing a machine learning model that is trained on the physics rather than data alone. These models are estimated to be orders of magnitude faster than the conventional simulators. Once the model is trained, it will replace some components in workflows with heavy computational loads.

Essential Responsibilities And Duties

As part of the numerical simulation team you will work on developing a physics informed machine learning model. This model will be trained on data generated by a coupled advection-geochemical numerical simulator. You will also integrating this model in full workflows and show that the workflows can be run orders of magnitude faster. You will work mostly with Python and Tensorflow and might get an opportunity to work with C++.

Qualifications

Penultimate or final year student, studying towards Bachelors or Masters in Computer Science, Chemical Engineering or related field.

Required Skills

Competencies:
  • Machine learning
  • Python
  • Numerical methods
Experience in one of the following would be advantageous
  • Knowledge of mass conservations equations and material balance
  • Simulation basics (numerical methods, computer science, etc)
  • Chemical Engineering, and the understanding of geochemical processes
  • C++
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.

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Deadline: 16-07-2024

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