We are seeking a talented, creative scientist to strengthen a multi-disciplinary team conducting applied research in the space of mathematical modelling, statistics, uncertainty quantification, optimization, and automated AI planning. The team is committed to deliver innovative platforms for advancing the automation of oilfield operations, which requires overcoming challenges in modelling and interpreting the behavior of nonlinear multi-physics environments, doing inference and optimization in high-dimensional systems with uncertainty, quantifying and handling uncertainty in decision-making, and discrete-mathematics reasoning for large operations decision-making.
We offer an environment to build a career that continuously broadens the Scientist’s mathematical skills through a diverse range of mathematically challenging applications, and expect the Research Scientists to actively seek to grow and be flexible to learn. Successful Research Scientists are excellent verbal and written communicators, and have the ability to work both independently and in a team.
• Conduct independent scientific research whilst collaborating with other scientists and experimentalists, under general supervision
• Develop proof-of-concept solutions based on own innovative ideas to real-world oilfield problems, guided by a business value proposition
• Monitor own area of scientific expertise and work to expand breadth of technical knowledge
• Develop extensive relationships within the Schlumberger technical community, Universities and the Schlumberger business and engineering organizations
• Publish research papers, internal technical reports and patents, and present your work
• Learn about the oilfield domain
• PhD degree in applied mathematics, computer science, statistics or related discipline
• Specialization in one or more of the following:
• analytical and numerical modelling and solvers
• discrete mathematics (e.g. combinatorics, graph theory)
• automated AI planning (solver advancements and PDDL domain modelling)
• probabilistic modelling and uncertainty quantification
• statistical model inversion
• Proficiency in algorithm development and working knowledge of programming languages, such as Matlab, Python, Java or C++
• Comfortable tackling open-ended problems, with an innovative approach to problem solving
• Interest in application to real-world problems
• Strong teamwork and communication skills
The Schlumberger Cambridge 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 Cambridge 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.