SNSF Consolidator Grants for two Basel researchers

With the Consolidator Grants, the SNSF supports highly qualified young researchers who wish to consolidate their scientific independence. Of the 19 projects approved by the SNSF, two will go to the University of Basel.

The research project by historian Dr. Moritz von Brescius examines the origins of the excessive consumption of resources in the 20th century using the example of tropical and synthetic rubber in their constant competition with other commodities. The project uses the industrial “Battle of Materials” as a perspective to trace the emergence of a momentous mindset: the invention of the supposed “underconsumption” of raw materials in the 20th century. The work traces the rise and power of the research and development (R&D) complex and examines how private commodity producers, governments, corporations, research institutes and marketing experts in Europe, the US and its colonies sought new applications and markets for raw materials - especially in times of economic crisis and overproduction. The work shows how various actors promoted rubber as a versatile material, contributing to material path dependencies, a surge in resource overexploitation, and the “Great Acceleration” of human impacts on global environments in the 20th century.

Moritz von Brescius studied history at the Free University of Berlin and the University of Oxford and received his doctorate from the European University Institute in Florence in 2015 with a multi-award-winning thesis. He then worked as a postdoc at the Universities of Konstanz, Bern, and Harvard University. Since 2021, he has been leading a research group at the University of Bern's Institute of History as an SNSF Ambizione Fellow. He will take up his project at the University of Basel’s Europainstitut in July 2025.

The research project led by computer scientist Prof. Dr. Aurélien Lucchi aims to improve the methods used to train deep neural networks, which are currently driving advancements in artificial intelligence (AI). Currently, training these neural networks involves collecting vast amounts of data and selecting numerous hyperparameters, a process often reliant on trial and error and not immune to human bias. The project seeks to address these challenges by deepening the understanding of the impact of these hyperparameters and devising automated methods for selecting them. The aim of this project is to make the optimization of AI models more energy-efficient and reliable.

Aurélien Lucchi has been an assistant professor of data analytics systems at the University of Basel since 2022. After completing his PhD at EPFL in 2014, he initially worked as a postdoc and, from 2018, as a senior researcher at the Department of Computer Science at ETH Zurich. He has broad academic experience in various areas of machine learning and optimization. His project at the Department of Mathematics and Computer Science at the University of Basel will start in November 2024.