PhD Student / PhD Studentess for MiningBrines DC16: Pore-scale reactive transport modelling for utilization of geothermal reservoirs

Your Tasks

The project -Reactive Transport Modeling (Hydraulic Chemical) at the Pore Scale and Upscaling to Reservoir Scale- aims to develop advanced pore-scale reactive transport modeling capabilities by enhancing an in-house lattice Boltzmann-based transport solver. This extension explicitly resolves microstructural evolution in reservoir rocks driven by mineral dissolution and precipitation. These simulations quantify how coupled flow, transport, and chemistry processes generate 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 to transfer pore-scale process understanding to geothermal reservoir scale reactive transport formulations while preserving key mechanistic controls.

To improve computational performance and multiphysics couplings, the work integrates AI/ML methods (e.g., neural networks, neural operators) to accelerate both pore-scale simulations and the associated upscaling workflows, facilitating realistic simulations, efficient parameter exploration, and reduced order model generation.

Expected Outcomes

• Implement a Lattice Boltzmann (LB) solver for modeling processes at pore scale in 2D/3D realistic multimineral pore structures (synthetic and tomography-driven).
• Develop surrogate models to accelerate simulation of chemistry and couple with LB.
• Quantify the impact of reactions and microstructural evolution on permeability and transport properties relevant to resource extraction and derive 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, personal development opportunities, and a strong vocational training culture. We support optimal work-life balance through modern employment conditions and robust on-site infrastructure.

MiningBrines: An international network of 32 academic and industrial partners across various disciplines offers an innovative doctoral training program to address Europe’s strategic need for sustainable access to critical raw materials, energy gases, and renewable energies. You will receive 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.

Application Process

Interested candidates are invited to apply online using the form below. Please include the following documents:

• Your CV
• A letter of application
• A 1-page critical summary of an article of your choice
• The names of two contact persons (former teachers or supervisors) who can provide a support letter
• Academic credentials

Only applications matching the job profile will be considered.

Important Dates

Application Deadline: April 30th, 5 PM CET
Expected Starting Date: October 1, 2026

Contact Information

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

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