PhD student in Data-Driven Modeling of Amorphous Materials for Sustainable Catalysis
Empa, Materials Science and Technology
Dübendorf
Key information
- Publication date:07 January 2026
- Workload:100%
- Contract type:Internship
- Place of work:Dübendorf
Job summary
Empa is a leading research institution focused on materials science. Join us for a unique opportunity to contribute to sustainable solutions.
Tasks
- Develop data-driven models for amorphous materials in catalysis.
- Utilize machine learning and simulations to predict material properties.
- Collaborate in an interdisciplinary team on innovative projects.
Skills
- MSc in Physics, Computational Chemistry, or related fields required.
- Strong programming skills and experience with numerical methods.
- Proven background in machine learning and data-driven modeling.
Is this helpful?
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.
PhD student in Data-Driven Modeling of Amorphous Materials for Sustainable Catalysis
Our Laboratory for Computational Engineering in Dübendorf is offering a position for two motivated doctoral students.
Your tasks
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.
Our offer
We offer a stimulating, multidisciplinary research environment within the ETH Domain, where communication and interaction to create synergies and develop novel ideas are highly valued. PhD students will have access to state-of-the-art computational infrastructure, benefit from internationally competitive employment conditions, and receive strong support for personal and professional development. The PhD students will be enrolled in the ETH Zürich doctoral program. The position is available from April 1st 2026 or upon agreement.
We live a culture of inclusion and respect. We welcome all people who are interested in innovative, sustainable and meaningful activities.
We look forward to receiving your complete online application including a letter of motivation, CV, certificates, diplomas and contact details of two reference persons. Please submit these exclusively via our job portal. Applications by e-mail and by post will not be considered.
