PhD position on AI methods for industrial catalysis

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description Industrial catalytic processes play a fundamental role in the production of fuels, chemicals and energy carriers. Their optimization is crucial for improving efficiency, reducing environmental impact and supporting the energy transition. However, conventional optimization still largely relies on expert-driven experimentation, which becomes increasingly time-consuming and expensive when dealing with complex systems involving numerous interacting variables and competing performance objectives. Recent advances in Artificial Intelligence offer exciting opportunities to overcome these limitations, by accelerating experimental discovery and enabling more efficient, adaptive and autonomous optimization strategies. We are looking for a motivated PhD candidate to work on state-of-the-art AI methods for sustainable industrial catalysis. The project will focus on the use of Sequential Learning and agent-based methods to guide experimentation efficiently in complex design spaces. The long-term vision is to contribute to the methodological foundations of self-driving laboratories. The research will be carried out at Politecnico di Torino in collaboration with Shell Global Solutions International B.V. Case studies will be defined based on industrial relevance. Periods of research at Shell’s Energy Transition Campus Amsterdam (ETCA) are foreseen. Where to apply E-mail [email protected]. Re