Research Scientist – Statistical Inference

Schlumberger Oilfield UK plc

The Schlumberger Gould Research Centre offers a stimulating research environment with real-world problems that push the limits of scientific knowledge. We are committed to be at the leading edge of science and to incorporate new emerging technologies in Schlumberger’s activities. To achieve that we recruit the most talented scientists from a variety of scientific and engineering backgrounds, and give them opportunities to advance their fields of expertise as well as to develop solutions of significant industrial impact.

The Schlumberger Gould Research Centre strongly encourages the self-development of its scientists, offers high-end experimental facilities and scientific resources, and maintains strong collaborations with academic and industrial research groups worldwide.

Schlumberger is currently developing new drilling systems with a high degree of autonomy and intelligence which will change the way the industry drills wells. Accessing hydrocarbon reservoirs often involves drilling and navigating in geometrically complex trajectories, through sometimes unpredictable physical environments, to reach targets more than 10 km from the drilling rig. As part of our commitment to innovation and research, we are seeking research scientists to focus on the fundamental science and technology needed to create autonomous drilling systems that handle the complex physics and mechanics of the drilling process.

Job Description

The monitoring and control programme is looking for Research Scientists with strong expertise in the area of statistical inference to be part of creating an autonomous drilling system. The successful candidate will develop the enabling technologies for monitoring, diagnosing and optimising the operation of a system with complex non-linear dynamics and high uncertainty. The job involves problem formulation, modelling, algorithm development, testing with experimental and real-world data, as well as integration with other aspects of an autonomous system such as control and planning.


  • Identify knowledge or technology gaps, and generate ideas to address those
  • Develop and apply statistical inference techniques to support various aspects of an autonomous drilling system (state and parameter estimation, diagnosis, control, and planning)
  • Test and validate solutions through simulations, full-scale experiments and oilfield data
  • Keep up to date and expand your knowledge in the field of expertise and the oilfield domain
  • Publish research papers, internal technical reports and patents, and present your work
  • Engage with business and engineering teams to ensure solutions have a significant impact


  • PhD degree in mathematics, computer science, control systems engineering, fault diagnostics and prognostics, or related fields
  • Understanding of various statistical model inversion techniques, in particular Bayesian inference and updating
  • Preferred experience in one or more of the following: hybrid state estimation; optimisation; combination of machine learning techniques with physics-based models; model reduction; advanced control techniques, such as model-predictive control; model-based approaches to diagnostics and prognostics; structural analysis
  • Competency in algorithm development and implementation
  • Experience in application to real-world problems
  • Strong teamwork and communication skills

Schlumberger 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, status as a protected veteran or other characteristics protected by law.

To apply, please send a CV and covering letter to with Research Scientist – SGR in the subject line.

Share this job
  Share by Email   Print this job   More sharing options
We value your feedback on the quality of our adverts. If you have a comment to make about the overall quality of this advert, or its categorisation then please send us your feedback
Advert information

Type / Role:


South East England