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 modeling capabilities by extending an in-house lattice Boltzmann-based transport solver. This initiative explicitly resolves microstructural evolution in reservoir rocks driven by mineral dissolution and precipitation. Through these simulations, we assess how the interlinked processes of flow, transport, and chemistry induce dynamic changes in pore geometry, transport properties, and reactive surface area under geothermal conditions.

Building upon these high-resolution models, the project establishes robust upscaling strategies to translate pore-scale process understanding into geothermal reservoir-scale reactive transport formulations, all while preserving key mechanistic controls.

To enhance computational performance and multiphysics couplings, the work integrates AI/ML methods (such as neural networks and neural operators) to accelerate both pore-scale simulations and the relevant upscaling workflows. This integration allows for 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 expedite the simulation of chemistry while coupling these with LB.
• Quantify the impact of reactions and microstructural evolution on permeability and transport properties relevant to resource extraction, 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 advantageous.
• 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 thrives on interdisciplinary, innovative, and dynamic collaboration. You will benefit from systematic training on the job, along with personal development opportunities and a strong vocational training culture. We provide modern employment conditions and support to optimally balance work and family life or other personal interests.

MiningBrines: Join an international network of 32 academic and industrial partners across various disciplines, offering 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. The position is funded for 36 months with a competitive salary, allowances, and additional funding for technical training and conference participation.

Interested candidates are invited to apply online using the form below.

Important Information

**Application Deadline:** April 30th, 5 PM CET. Only applications matching the job profile will be considered.

**Expected Starting Date:** October 1, 2026.

**For Further Information:** Please contact Dr. Nikolaos Prasianakis at nikolaos.prasianakis@psi.ch.

Contact:

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