Scientific Software Engineer Intern (3 months) Abingdon - United Kingdom Job Title: Scientific Software Engineer Intern (3 months) - Starting Summer 2026 _Project Title: ??Asphaltene Fluid Modeling and Equation of State Tuning?_ 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: Abingdon, Oxfordshire Description & Scope: ?? ?Classical thermodynamics forms the foundation for modeling a wide range of engineering applications, particularly in the oil and gas industry. The behavior of fluid and solid systems is often described using Equation of States (EoS), which capture the relationship between pressure, volume, temperature and composition. For pure components, well-established EoS models exists, calibrated using laboratory data, thereby reducing the need for extensive tuning in practical applications. However, modeling the behavior of mixtures, particularly hydrocarbon mixtures, presents a significant challenge. The presence of numerous components, including isomers, makes it impractical to model each component individually. This complexity is further amplified in systems involving asphaltenes - highly complex and poorly characterized fractions of crude oil. To address these challenges, techniques such as lumping and tuning are employed to simplify real-component systems into pseudo-component mixtures. These approaches aim to retain the key thermodynamic behaviours of the system while significantly reducing computational costs. In this context, we can write the tuning as a data-assimilation problem and solve it by applying different methods. This internship focuses on tackling the tunning problem for asphaltene modeling using various EoS formulations, including Peng-Robinson, Soave-Redlich-Kwong and Cubic Plus Association (CPA). The goal is to develop a robust workflow that convert asphaltene PVT laboratory data into EoS inputs applicable to real-world oil industry scenarios.? Responsibilities ?As part of this internship, the candidate will collaborate closely with the Intersect Physics team and undertake the following responsibilities: Literature review: conduct an in-depth review of the state-of-the-art in asphaltene modeling, PVT laboratory data interpretation, and Equation of State tuning techniques Data tuning: implement and apply tuning methodologies to real asphaltene PVT laboratory datasets Workflow development: design and document a systematic workflow for converting asphaltene PVT laboratory data into EoS inputs Validation and comparison: evaluate and compare the performance of different EoS models, identifying strengths and limitations for each Manuscript preparation: prepare a detailed manuscript summarizing the workflow, methodology, and key findings. The manuscript will serve as a basis for potential submission to a research journal? Qualifications : Studying a Masters in ?Chemical Engineering, Fluid Modeling, Applied Physics? or a related discipline Thermodynamics and fluid modelling Data analysis Programming (Python) Scientific documentation? _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._