At the Energy Division of NORCE Research there is a 3-year full-time PhD research fellow position available within the field of Accelerated Computational Modelling of Reactive Flow in the Subsurface. The position is for a fixed-term period of 3 years at NORCE starting August 2026. You will be enrolled as a PhD student at Faculty of Science and Technology at the University of Stavanger and be affiliated with the research group Computational Geosciences and Modelling at NORCE Energy Department. The location of the position is either Stavanger or Bergen. The student is expected to spend significant time in both locations.

About the project/work tasks

The project will focus on reactive flow in porous rocks, with applications to CO₂ storage, carbonated-water injection and long-term use of subsurface reservoirs. A key challenge is that simulations become computationally demanding when multiphase flow, phase behavior, geochemical reactions and changes in rock properties are coupled.

The PhD project will contribute to the further development of IORSim, a reservoir simulation tool developed by NORCE in collaboration with IFE and the University of Stavanger. Collaboration with industry experts is also expected as part of this project. The work includes numerical model development, simulator implementation, testing against laboratory or field data, and coupling with open-source reservoir simulation tools such as OPM – Open Porous Media. An important part of the project will be to explore how machine learning, reduced-order models or surrogate models can accelerate reactive transport simulations while preserving the most important physical and chemical constraints.

Possible research questions include:

• How can reactive transport simulations be made faster using machine learning, reduced order models or surrogate models without losing essential physics and chemistry?
• How can geochemical reactions be efficiently coupled with multiphase reservoir simulation?
• How do fluid-rock reactions affect porosity, permeability, injectivity and long-term flow paths?
• Can data-driven methods support parameter selection, model calibration, optimization or uncertainty analysis?

Qualifications

• Applicants must hold a master's degree or equivalent education in applied and computational mathematics, scientific computing, computational physics, computational geoscience, or reservoir engineering.
• Master students can apply provided they complete their final master exam before August 2026. It is a condition of employment that the master's degree has been awarded.
• A documented background in fluid dynamics, computational modeling, and scientific programming is required.

The following areas of expertise or documented experience are considered beneficial:

• Scientific computing using Python, C, or C++.
• Collaborative open-source software development.
• Fluid flow and transport for multiphase fluid flow porous media.
• Geochemical modeling
• Reservoir simulation
• Machine learning