Research Scientist – Optimisation Science

Research Scientist – Optimisation ScienceAbout Us:At SLB our purpose is to create amazing technology that unlocks access to energy for the benefit of all. As innovators, that’s been our mission for 100 years. We are facing the world’s greatest balancing act - how to simultaneously reduce emissions while meeting the world’s growing energy demands. We’re working on that answer. Every day is a step closer. Our collective future depends on decarbonizing the fossil fuel industry while innovating across the entire energy landscape. It’s what drives us, ensuring progress for the people and the planet - on the journey to net zero and beyond.With a global footprint in more than 100 countries and employees representing almost twice as many nationalities, we work each day on decarbonizing industry, innovating in oil and gas, delivering digital at scale, and developing and scaling new energy systems that accelerate the energy transition.We’re looking for innovators to join our diverse community. If you share our passion for discovery we’d like you to apply.

Location: Cambridge, UKSLB 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 laboratory and computing facilities. We work on applied research projects in the physical sciences to meet the current and future challenges of the industry. At SCR, we strike a balance between academia and industry with projects from early-stage research all the way to solution development for real business challenges.SCR is a distinctive marquee-like structure dominating the High Cross site on the western outskirts of Cambridge, England. The centre provides extensive laboratory and office facilities and is home to more than 100 scientists, technicians and domain experts.Job Summary: We are seeking a creative and self-motivated Research Scientist with expertise in optimisation, particularly multi-objective optimisation, and decision science. The scientist will develop optimisation methods for complex engineering and operational systems involving multiple, often competing objectives, constraints, uncertainties, and decisions across different temporal and spatial scales. The successful candidate will work as part of a multidisciplinary research team, combining optimisation with experiments, physical and mathematical models, data-driven methods, and domain knowledge. The research will support the understanding, design, operation, and control of complex systems and the development of new technologies.

Responsibilities

Develop and apply optimisation methods for complex engineering, process, and operational systems.Formulate and solve decision problems involving multiple objectives, constraints, uncertainties, and trade-offs.Develop and integrate optimisation approaches with physics-based, reduced-order, surrogate, and data-driven models.Develop computationally efficient methods for optimisation under uncertainty, including robustness and sensitivity analysis.Contribute to optimisation of system design, operating strategies, measurement selection, resource allocation, and control.Work collaboratively with experimental scientists, mathematical modellers, data scientists, and domain experts to translate scientific and engineering challenges into suitable optimisation problems.Communicate findings through technical reports, publications, conference presentations, patent applications, and engagement with internal and external collaborators.

Qualifications

PhD in operational research, applied mathematics, optimisation, engineering, computer science, or a related discipline.Strong expertise in mathematical optimisation, including multi-objective optimisation and decision-making under uncertainty.Experience applying optimisation methods to complex engineering or scientific problems, including integration with simulation, surrogate, and data-driven models.Strong scientific programming skills and experience developing optimisation workflows.Ability to work effectively in multidisciplinary teams.Experience in dynamic systems, process optimisation, optimal experimental design, resource allocation, decision support, or optimisation-based control would be advantageous.SLB is an equal employment opportunity employer. Qualified applicants are considered without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, age, disability, or other characteristics protected by law.