Geothermal Research Intern (12 months)

Schlumberger Limited; Cambridge; Presencial; GBP 15 000 - 21 000; Tempo parcial

Resumo da oferta

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.

Qualificações

• 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.

Responsabilidades

• 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.

Conhecimentos

Numerical methods

Probability and statistics

Data analysis

Formação académica

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

Ferramentas

Python

Descrição da oferta de emprego

IT & Digital Technology, Engineering and Manufacturing, Supply Chain and Logistics

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,multidisciplinaryenvironment 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 inApplied 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.

We believe your best work comes from feeling supported, empowered, and connected – wherever you are in your journey. We’re committed to providing you with the resources and culture to help you belong, advance, and succeed.

Culture and Values

For more than 100 years, we've been at the forefront of energy innovation; solving problems considered impossible while building a safe and inclusive culture.

We support our people where and when they want to grow by making continuous learning a key priority.

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Find quick answers to common questions about roles, teams, and the application process.

We are a global technology company, driving energy innovation for a balanced planet. Together, we create amazing technology that unlocks access to energy for the benefit of all.

Global in outlook, local in practice – and with a united, shared passion for discovering solutions, we hire talented, driven people and support them to succeed, personally and professionally.

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.

We will endeavor to make a reasonable accommodation / modification to the known physical or mental limitations of a qualified applicant with a disability to assist in the hiring process, unless the accommodation would impose an undue hardship on the operation of our business, in accordance with applicable federal, state, and local law. If you believe you require such assistance to complete this form or to participate in the interview process, please contact [e-mail] to request assistance. Please note that only those inquiries concerning a request for reasonable accommodation will be responded to.

SLB is a VEVRAA Federal Contractor- priority referral Protected Veterans requested.