Postdoctoral Researcher - Fluid Flow and Heat Transfer Modelling

Schlumberger
Cambridge
11 months ago
Applications closed

Related Jobs

View all jobs

Postdoctoral Researcher in Machine Learning analysis of MRI

Postdoctoral Researcher in Machine Learning analysis of MRI

Postdoctoral Researcher in Machine Learning analysis of MRI

Big Data Science & Analytics Intern - Tesco

Big Data Science & Analytics Intern

Applied Machine Learning Intern - Tesco

Job title:

Postdoctoral Researcher (FTC) - Fluid Flow and Heat Transfer Modelling

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 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 company website

Location:

Cambridge, England

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.

SLB Cambridge Research is a distinctive marquee-like structure dominating the High Cross site on the western outskirts of Cambridge, England. This spectacular building was completed in 1985 and the smaller second phase was opened in 1992. It has recently been classified as a Grade II listed building. Altogether, there are more than 930 m2; of laboratory space and offices for more than 100 scientists, technicians and domain experts.

Job Summary:

Supporting the global transition to cleaner and more sustainable energy sources, our multidisciplinary team is working on novel approaches to geothermal energy extraction, overcoming geographic limitations of conventional systems to enable scalability and accessibility worldwide. We are looking for a creative and self-motivated scientist who will contribute to the development of advanced models and methods for simulating fluid flow and heat transfer in such geothermal systems. A key focus will be providing critical insights into hydrodynamic phenomena and thermal processes through CFD simulations and simplified mathematical models.

Typical Responsibilities and Duties:

  • Conduct CFD simulations and perform detailed analyses of complex 3D turbulent flow and heat transfer.
  • Develop simplified mathematical models based on CFD insights for integration into the system-level frameworks to evaluate geothermal designs.
  • Validate models and results against theoretical benchmarks, experimental data, and real-world observations.
  • Prepare high-quality technical reports, scientific papers, and patent applications.
  • Present results to peers, stakeholders and at scientific conferences.
  • Collaborate with the wider SLB technical community.


Qualifications:

  • PhD in Fluid Mechanics or a related fields in mathematics, engineering or physical sciences.
  • Proven expertise in CFD simulations, preferably involving turbulent heat transfer.
  • Strong ability to develop mathematical models for fluid flows.
  • Proficiency in C++ or Python programming.
  • A track record of publications in reputable peer-reviewed journals.
  • Excellent written and oral communication skills.
  • Ability to work in a team and conduct independent research.


Desirable Skills

  • Proficiency in CFD software, such as OpenFOAM, Ansys, or COMSOL.
  • Experience in conducting resource-intensive CFD simulations.
  • Knowledge of physics-based machine learning and reduced-order modelling techniques.


What we can offer you:

Competitive base salary with bonus, private healthcare for employee & family, subsidised dental care, Health & Wellbeing programs such as the Employee Mental health support, health & wellness coaching, part employer and employee funded pension contribution, Income protection scheme, life insurance.

Other benefits are also available through the SLB flexible benefits program.

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.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.