PhD position on AI methods for industrial catalysis

Politecnico di Torino; Italia; On-site; EUR 30,000 - 42,000; Full time

Job summary

Politecnico di Torino in Italy invites applications for a PhD candidate position in AI for sustainable industrial catalysis. The project focuses on sequential learning and agent-based methods to guide experimentation in complex design spaces, with the long-term aim of self-driving laboratories.

The research will be conducted at Politecnico di Torino in collaboration with Shell Global Solutions International B.V. Case studies will be defined based on industrial relevance.

Qualifications

• Background in engineering, physics, chemistry, or related disciplines.
• Basic knowledge of energy processes, AI methods and scientific programming (Python, MATLAB or similar).
• Strong analytical and problem-solving skills; eager to learn and work in an international academic-industrial environment.

Skills

Analytical thinking

Interdisciplinary research

Education

Master Degree or equivalent

Tools

Python

MATLAB

Job description

Organisation/Company Politecnico di Torino Department Energy Department Research Field Engineering » Materials engineering Engineering » Chemical engineering Physics » Computational physics Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 1 Feb 2027 - 23:59 (Europe/Rome) Country Italy Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

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.

Requirements

Research Field Engineering » Materials engineering Education Level Master Degree or equivalent

Skills/Qualifications

Applications from motivated candidates with a background in engineering, physics, chemistry, or related disciplines are welcome. Basic knowledge of energy processes, AI methods and scientific programming (Python, MATLAB or similar) are desirable. Strong analytical and problem‑solving skills, scientific curiosity and enthusiasm for interdisciplinary research are particularly valued. Previous experience in AI‑driven optimization or catalytic processes is a plus, but not mandatory. Above all, we look for candidates that are eager to learn, willing to develop new ideas and work in an international academic‑industrial environment.