Geothermal Research Intern (12 months)
Confirmed on the employer's own hiring board on Oct 29, 2026. First seen by Alion on Oct 22, 2026. SLB scores A on the Alion truth index.
Job Title:
Geothermal Research Intern (12 months) - Starting Summer 2027
Project Title: Uncertainty Quantification in Enhanced Geothermal Systems
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 https://www.slb.com/who-we-are
Location:
Cambridge, UK
SLB Cambridge Research (SCR) is part of SLB’s global network of research and engineering centres. SCR is a dynamic, multidisciplinary environment with state-of-the-art research and computing facilities. We work on applied research projects in the physical sciences to meet the current and future challenges of the industry.
Description & Scope:
Geothermal models depend on subsurface properties that are difficult to measure. More detailed models can represent additional physical processes, but their value for prediction may be limited by uncertainty in the available data.
The intern will use numerical modelling and statistical analysis to investigate how uncertain inputs and modelling assumptions affect geothermal predictions. The work will focus on a case study, comparing models of different complexity to assess when additional model detail is useful and when better measurements would be more valuable. In particular, the case study will examine when relationships derived from small-scale simulations and experiments improve reservoir-scale predictions.
Responsibilities:
Review measurement options and modelling approaches relevant to the selected case study
Carry out a computational study to quantify prediction uncertainty
Compare predictions from models of different complexity with observations
Prepare documented code and a report recommending where additional measurements or more detailed modelling would be most useful
Requirements :
Studying a Bachelors or Masters in Applied Mathematics, Computer Science, Physics, Engineering, Geosciences or a related subject
Foundation in numerical methods, probability and statistics
Programming skills in Python or a similar language
Ability to analyse data and explain results clearly
Familiarity with one or more of the following would be useful: uncertainty quantification methods, subsurface measurement techniques, and fluid mechanics and heat transfer.
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.
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.