Geothermal Research Intern (12 months)

SLB; Cambridge; On-site; GBP 12,000 - 18,000; Full time

Job summary

SLB is seeking a Geothermal Research Intern (12 months) for a summer 2027 start at the Cambridge Research facility. The role focuses on uncertainty quantification in enhanced geothermal systems, using numerical modelling and statistics to study how input uncertainties affect reservoir-scale predictions.

The intern will compare models of different complexity and contribute to documentation, code preparation, and a final report identifying valuable measurements or modelling approaches.

Qualifications

• Studying for a Bachelor or Master in Applied Mathematics, Computer Science, Physics, Engineering, Geosciences or related field.
• Foundation in numerical methods, probability and statistics.
• Programming in Python or similar language.
• Ability to analyse data and explain results clearly.

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.

Skills

Python

Numerical methods

Statistics

Data analysis

Education

Bachelor's/Master's in a relevant field

Tools

Python

Job description

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.

The recruiting process and the position can be adapted to fit most disabilities, please do not hesitate to mention this when applying.