Geothermal Research Intern: Uncertainty Quantification

Schlumberger Limited; Cambridge; On-site; GBP 15,000 - 21,000; Part time

Job summary

Schlumberger Limited in Cambridge, UK invites applications for a Geothermal Research Intern (12 months) starting Summer 2027. The project focuses on Uncertainty Quantification in Enhanced Geothermal Systems and will involve numerical modelling and statistical analysis.

You'll review measurement options, run computational studies, compare model predictions with observations, and document code with a report containing actionable recommendations for improving measurements or modelling detail.

Qualifications

• Currently enrolled in a Bachelor’s or Master’s 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 uncertainty quantification methods, subsurface measurement techniques, and fluid mechanics and heat transfer is useful.

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

Numerical methods

Probability and statistics

Data analysis

Education

Bachelor’s or Master’s in Applied Mathematics, Computer Science, Physics, Engineering, Geosciences or related

Tools

Python

Job description

Schlumberger Limited in Cambridge, UK invites applications for a Geothermal Research Intern (12 months) starting Summer 2027. The project focuses on Uncertainty Quantification in Enhanced Geothermal Systems and will involve numerical modelling and statistical analysis.

You'll review measurement options, run computational studies, compare model predictions with observations, and document code with a report containing actionable recommendations for improving measurements or modelling detail.