Reactive transport modeling; Hydraulic Chemical pore scale and upscaling to reservoir scale

Organisation/Company Paul Scherrer Institut (PSI) Research Field Computer science » Modelling tools Geosciences » Other Computer science » 3 D modelling Researcher Profile First Stage Researcher (R1) Positions PhD Positions Final date to receive applications 30 Apr 2026 - 17:00 (Europe/Paris) Country Switzerland Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Sep 2026 Is the job funded through the EU Research Framework Programme?

Horizon Europe - MSCA Marie Curie Grant Agreement Number  Is the Job related to staff position within a Research Infrastructure? No

PhD

Position Title:

Reactive transport modeling (Hydraulic Chemical) at the pore scale and upscaling to reservoir scale.

Doctoral Candidate (DC) Number: 16

Work Package Number: 5

Host Institution (full legal name): Paul Scherrer Institute (PSI)

Department / Research Group: Center for Nuclear Engineering and Sciences / Laboratory for Waste Management

Research activities Start Date (expected): September 1st 2026

Duration: 36 months

Mining Brines offers an innovative doctoral training program to address Europe's strategic need for sustainable access to critical raw materials (CRM), energy gases (EG) and renewable energies. 19 Doctoral Candidates (DCs) will receive interdisciplinary training in geosciences (Work Packages 2 and
3), biogeochemistry (Work Package
4), artificial intelligence (AI) (Work Package
5), and socio-economic analysis (Work Package
6), equipping them with advanced skills in reservoir modeling, machine learning, advanced oxidation processes (AOP), and microbial enhanced recovery. DCs will also develop intuitive fluid chemistry modeling workflows and innovative multi-criteria intelligent decision support tools, preparing them to drive innovation in geothermal brine mining while collaborating with academic and industrial partners on practical solutions.

Mining Brines introduces novel techniques to maximize geothermal multi-resource recovery while minimizing environmental impact. Key innovations include microbial-driven CRM recovery, customized AOP workflows, scalable AI models, and decision support tools that consider technological, economic, and societal aspects. These advances aim to reduce the environmental footprint of resource extraction and align with the sustainability goals of the EU Green Deal.

Mining Brines supports the EU's Critical Raw Materials Act by combining CRM and EG recovery with renewable energy production and circular economy principles, reducing Europe's import dependency and strengthening resilience. In addition, Mining Brines emphasizes collaborative education to meet the growing demand for skilled professionals capable of transforming geothermal multi-resources into a key driver of Europe's green transition.

The impact of Mining Brines goes beyond scientific advances, fostering a skilled workforce for academic and industrial sectors, while establishing Europe as a global leader in sustainable resource management.

Mining Brines promotes public awareness of the multiple benefits of geothermal energy, setting a standard for green industrial practices and long-term strategic autonomy.

The project aims to develop advanced pore‑scale reactive transport modelling capabilities for geothermal reservoir applications by extending an in‑house lattice‑Boltzmann (LB) transport solver to explicitly resolve microstructural evolution driven by mineral dissolution and precipitation. The central objective is to quantify how coupled flow, solute transport, and geochemical reactions dynamically modify pore geometry, reactive surface area, permeability, and effective transport properties under geothermal conditions.

A further objective is to establish robust upscaling strategies that transfer mechanistic understanding gained at pore scale to reservoir‑scale reactive transport formulations, while preserving the essential physical and chemical controls. The project also seeks to enhance computational efficiency and multiphysics coupling through the integration of artificial intelligence and machine learning (AI/ML) techniques,…