Refine your search

Diana Arbelaez Ruiz ([email protected])

I study the social and political dimensions of resource extraction to inform dialogue, and policy- and decision-making. My interests include the dynamics of raw materials for the energy transition, conflict and peacebuilding in mining regions, and indigenous rights and activism in natural resource extraction contexts. I have more than 20 years’ combined experience in the areas of development, social responsibility, peace and conflict studies, and sustainability, with a strong emphasis on the extractive sector.

At UEF, I am examining the geopolitical and socio-environmental aspects of energy transition minerals from a global perspective. My previous posting was as Senior Research Fellow in the Sustainable Minerals Institute’s Development Minerals Program, where I oversaw the establishment of an online knowledge exchange network for ASM miners and quarry workers – the Delve Exchange. My doctoral thesis dealt with Indigenous community participation in post-conflict mineral resource governance in Colombia. As part of this, I was a Visiting Endeavour Fellow at the Peace Research Institute Oslo (PRIO). Subsequently, I was a Rotary Peace Fellow at Chulalongkorn University in Bangkok. I held Research Fellow and Research Manager roles at the Centre for Social Responsibility in Mining, Sustainable Minerals Institute, University of Queensland, where I worked on a broad suit of topics focusing on Latin American and Australian sites. I have been a consultant to NGOs and mining companies providing specialist knowledge and advisory services in connection to mining and development projects.

 

Lyydia Meuronen ([email protected])

I’m working as a doctoral researcher at the Computational Physics and Inverse Problems research group at the UEF. In my thesis, I work with machine learning-based surrogate modeling for risk assessment and water quality prediction at mining sites. Waste rocks at the mining sites cause an environmental problem as leaching releases acid and harmful substances from the rocks which contain elevated levels of heavy metals and at atmospheric conditions chemically reactive minerals. Current reactive transport models (RTMs) for leaching are computationally expensive, so we intend to replace those models with computationally cheaper models while utilizing machine learning.

In my thesis work, I’m using models and measurements related to my study provided by my collaborator Geological Survey of Finland (GTK).