Aurora

Employment & Opportunities

Teaching Fellow - Imperial College London 

The Earth Science and Engineering (ESE) Department at Imperial College London are looking to hire a Teaching Fellow to support our computer-science focused MSc's, in particular a new MSc course we will be starting in October 2025- Renewable Energy with AI and Data Science: Geology and Geophysics (READY).  

READY MSc will have a focus on the application of data science and AI for charactering the subsurface for renewable energy infrastructure. This will involve geology, shallow geophysics and geotechnics. We are seeking a Teaching fellow to help us develop the course material and teach on the course from Oct 2025. This is a full-time, permanent position.

You can find the full job description here Description | Jobs | Imperial College London and more details on the course webpage.  Please contact Rebecca Bell for more information. The deadline is 1st Sept 2024.

News & Events

Research Seminar: Peyman Mostaghimi, Monday August 19, 3pm-4pm, ESB 5108

Title of the presentation: Can computers be trained as porous media experts?

Abstract:

Transport phenomena in geological formations occur within tiny pore spaces, and pore-scale imaging and modelling can elucidate the associated physical and chemical mechanisms. X-ray micro-CT imaging and pore-scale modelling have developed rapidly over the last two decades, bridging the disciplines of geology, chemical engineering, image processing, and computational fluid dynamics. They have provided new pathways for understanding complex phenomena in underground geological formations and other porous materials. However, several steps in this framework are time-consuming and subject to user bias. Machine learning and Convolutional Neural Networks (CNNs), as part of the broader field of Artificial Intelligence (AI), can be integrated into the framework of pore-scale modelling and imaging. The trade-off between sample size and image resolution, image segmentation, as well as the expensive computational cost associated with numerical simulations of fluid flow in the pore spaces, can be addressed using CNNs. I will demonstrate how we can recreate porous media images at super-resolution and explore porous media transport phenomena. Additionally, I will demonstrate the application in reactive transport modelling and discuss reliability and accuracy of CNNs in determining rock properties. Finally, I will discuss challenges and opportunities for the development of machine-learning approaches in porous media applications.

Biography: Peyman Mostaghimi

Peyman Mostaghimi is a Professor of Civil and Environmental Engineering at the University of New South Wales (UNSW), Australia, where he leads a multidisciplinary research group on Multiscale Transport in Porous Systems (MUTRIS). He is the Chair of the Council of the International Society for Porous Media. His research is focused on fluid dynamics and transport phenomena in porous media with application to geological carbon dioxide storage, subsurface hydrology, hydrogen storage, minerals and hydrocarbon recovery and groundwater modelling. He performs theoretical, numerical, and experimental research into the characterisation of heterogeneous porous materials at different scales. He formerly was a Senior Fulbright Scholar at Harvard University. He obtained his PhD from Imperial College London on transport phenomena in porous media.