PhD position in machine learning for photocatalysis

Organisation/Company ETH Zürich Research Field Chemistry » Other Computer science » Other Engineering » Chemical engineering Physics » Chemical physics Physics » Other Researcher Profile First Stage Researcher (R1) Country Switzerland Application Deadline 11 Aug 2025 – 21:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 41 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? NoOffer DescriptionPhD position in machine learning for photocatalysisThe Digital Chemistry Laboratory is led by Prof. Dr. Kjell Jorner at the Institute of Chemical and Bioengineering, within the Department of Chemistry and Applied Biosciences at ETH Zurich and associated with the ETH AI Center. We are an interdisciplinary group at the intersection of chemistry and computer science. Our mission is to accelerate chemical discovery using digital tools. We predict chemical reactivity and molecular properties using machine learning, artificial intelligence, computational chemistry, and cheminformatics. Ourultimate goalis the computer-aided design of molecules and catalysts.Project backgroundCycloaddition reactions are among the most valuable synthetic tools for building molecular complexity, as they can form ring systems with high atom economy, contributing to innovations in,e.g., materials science and pharmaceuticals. Recently, energy transfer photocatalysis (EnT) has emerged as a ground-breaking method for facilitating cycloadditions. However, the reactivity and selectivity of substrates in EnT-catalyzed reactions remain challenging to predict, given the limited mechanistic understanding and scarcity of experimental data.This project aims to systematically address these challenges by building chemistry-informed machine learning models for selectivity and reactivity prediction. These models will also provide valuable mechanistic insights that enable the generalization of selectivity trends across diverse reaction conditions and substrates. We will also turn the models into user-friendly tools that will equip synthetic chemists with a robust predictive framework that enables precise prediction of reaction outcomes.The project is part of an international collaboration with the German Priority Program on the “Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning” (SPP 2363 ) and will involve tight collaboration with the group of Prof. Dr. Frank Glorius from the University of Münster, world-leading experts in photocatalysis and molecular machine learning. This collaboration will involve using the models in synthetic method development carried out in the Glorius group, including the selection of further reactions to run, and their application to targets of medicinal interest.As a PhD student in our growing team, you will develop machine learning methods to predict the reactivity and selectivity of energy-transfer-catalyzed photocycloaddition reactions. You will furthermore identify descriptors for photochemical reactions that can be used by the models to generalize better to new substrates and reaction types. You will be expected to collaborate closely with our experimental partners in the group of Prof. Dr. Glorius. Further responsibilities include contributing to the teaching activities of our department.ProfileWe are looking for a committed and motivated candidate that is excited to push the boundaries of research in digital chemistry.Essential experience, skills, and characteristics:A master’s degree in either chemistry, chemical engineering, computational science, materials science, physics, or related fields, or expectation of obtaining such a degree before the expected starting date of September 1Self-motivation, ability to work independently and solution-oriented mentalityInterdisciplinary and collaborative mindset and desire to work with people from different disciplines and backgroundsProgramming experience using languages such as Julia, Python, R, etc.At least one of the following:Experience in applying machine learning from research projects or thesisExperience in quantum-chemical simulations from research projects or thesisDesirable but not necessary criteria:Experience in organic synthesis from research projects or thesisWe offerYou will join a dynamic, and growing research group in the emerging field of Digital Chemistry and in the highly motivating environment of ETH Zurich. We foster a modern and supportive group culture and value diversity, independence, and initiative. The position is embedded in an exciting and interdisciplinary research environment with connections to the ETH AI Center and the National Centre of Competence in Research (NCCR) Catalysis, connecting the chemical sciences, digitalization, and sustainability.We look forward to receiving your online application untilMay 31, including:Copies of BSc and MSc educational recordsPlease note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.Further information about the group can be found on our website . Questions regarding the position should be directed to Prof. Dr. Kjell Jorner by email atkjell.jorner@chem.ethz.ch . Any applications that come in via email will be disregarded.About ETH ZürichETH Zurich is one of the world’s leading universities specialising inscience and technology. We are renowned for our excellent education,cutting-edge fundamental research and direct transfer of new knowledgeinto society. Over 30,000 people from more than 120 countries find ouruniversity to be a place that promotes independent thinking and anenvironment that inspires excellence. Located in the heart of Europe,yet forging connections all over the world, we work together todevelop solutions for the global challenges of today and tomorrow. #J-18808-Ljbffr