PhD student in Data-Driven Modeling of Amorphous Materials for Sustainable Catalysis

Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living. Empa is a research institution of the ETH Domain

Amorphous materials play a key role in sustainable catalysis and energy conversion, but their disordered atomic structure makes them difficult to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore how local atomic structures influence material properties and catalytic behavior. You will gain experience in cutting-edge computational methods, contribute to a predictive understanding of disordered materials, and work on applications such as CO₂ conversion, helping to advance sustainable chemical processes. Beyond learning, you will have the opportunity to contribute to groundbreaking discoveries and generate new scientific knowledge at the forefront of materials science, machine learning, and catalysis. This is an exciting opportunity to tackle a fundamental scientific challenge while developing skills that will shape your research career. Your profile

We are looking for 2 highly motivated PhD students with a strong analytical background and an MSc degree in Physics, Computational Chemistry, Materials Science, Computer Science, or a related disciplines. The candidates should have :

• A solid theoretical background in their field
• Strong programming skills and experience with numerical methods.
• Proven experience in machine learning and data-driven modeling.
• Ability to work independently as well as collaboratively in an interdisciplinary team.
• Excellent command of English, both written and spoken.
• Previous experience in atomistic simulations and catalysis is an advantage.

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