PhD Student / PhD Student

Your Tasks

The project Reactive Transport Modeling (Hydraulic Chemical) at the Pore Scale and Upscaling to Reservoir Scale aims to develop advanced capabilities in pore scale reactive transport modeling. By enhancing an in-house lattice Boltzmann-based transport solver, we will explicitly resolve microstructural evolution in reservoir rocks as influenced by mineral dissolution and precipitation. These simulations are designed to quantify how coupled flow, transport, and chemical processes induce dynamic changes in pore geometry, transport properties, and reactive surface area under geothermal conditions.

Building on these high-resolution models, the project establishes robust upscaling strategies that seamlessly transfer pore scale process understanding to geothermal reservoir scale reactive transport formulations while maintaining key mechanistic controls.

To enhance computational performance and multiphysics couplings, this research will integrate AI/ML methods (e.g., neural networks, neural operators) to rapidly accelerate pore scale simulations and associated upscaling workflows. This integration will enable realistic simulations, efficient parameter exploration, and the generation of reduced order models.

Expected Outcomes

• Implement a Lattice Boltzmann (LB) solver for modeling processes at the pore scale in 2D/3D realistic multimineral pore structures (both synthetic and tomography-driven).
• Develop surrogate models to accelerate the simulation of chemistry and couple them with the LB solver.
• Quantify the impact of reactions and microstructural evolution on permeability and transport properties pertinent to resource extraction, while deriving mechanistic correlations for upscaling.
• Integrate AI/ML-assisted sensitivity analysis and uncertainty quantification to evaluate prediction reliability and account for input data variability.

Your Profile

Required Qualifications:

• Master's degree (or equivalent) in a relevant discipline.
• Strong motivation for interdisciplinary research.
• Excellent command of spoken and written English (mandatory).

Desirable Skills:

• Programming skills (e.g., Python or C); familiarity with machine learning techniques is a plus.
• Background in porous media flows or CFD; experience with the lattice Boltzmann method would be beneficial.
• Knowledge of geochemical modeling and reactive transport fundamentals.

We Offer

PSI: Our institution fosters interdisciplinary, innovative, and dynamic collaboration. You will benefit from systematic on-the-job training, alongside personal development opportunities and a strong vocational training culture. We are committed to supporting a healthy work-life balance, providing modern employment conditions and on-site infrastructure to cater to your personal interests.

MiningBrines: Join an international network of 32 academic and industrial partners across multiple disciplines, offering an innovative doctoral training program designed to address Europe’s strategic need for sustainable access to critical raw materials, energy gases, and renewable energies. You will gain interdisciplinary training in geosciences, biogeochemistry, artificial intelligence, and socio-economic analysis. This position is funded for 36 months with a competitive salary, allowances, and additional funding for technical training and conference participation.

Apply online using the form below. Only applications matching the job profile will be considered.

Additional Information

For further information, please contact Dr. Nikolaos Prasianakis at nikolaos.prasianakis@psi.ch.

Paul Scherrer Institute, Human Resources Management, Serdal Varol, 5232 Villigen PSI, Switzerland.