Your tasks
This project aims to develop, implement, and validate a fully autonomous route for the optimization of the growth of epitaxial oxide thin films using physical vapor deposition. Multiple in situ characterization techniques will be employed, monitoring quality indicators of the films while they are growing and allowing for the live tuning of the growth parameters.
You will be responsible for developing a hardware-software interface for autonomous thin film growth (including both the operation of the chamber and the monitoring of the in situ techniques). You will combine these interfaces with machine learning approaches, exploring optimization algorithms to control the structural and chemical properties of the resultant thin films, aiming at determining the parameters for crystalline and stoichiometric epitaxial growth. The resulting code that you develop, and implement will be integrated into an autonomous platform to drive the search for the ideal growth conditions. Subsequently, you will demonstrate and validate the developed models through the autonomous growth optimization of functional oxide thin films. The infrastructure, the methods and the collected data will be published in peer reviewed articles.
You will be enrolled in the Materials Science and Engineering Doctoral program at EPFL, from which you will receive your PhD title. The doctoral candidature will involve in-person coursework at EPFL in Lausanne.
Your profile
Candidates are sought with a background in the physical sciences or engineering, alongside a passion for programming. Candidates are expected to show excellent work ethics and to feel at home working in teams. Female candidates are strongly encouraged to apply.
Requirements for the candidates are:
- Master’s degree in Physics, Chemistry, Engineering or Materials Science
- Strong programming skills (ideally in Python, but advanced knowledge of other programming languages will also be considered)
- Strong motivation for materials science and discovery, for working in a team and a passion for automation of repetitive tasks
- Excellent communication skills in written and spoken English (knowledge of German is a plus but not required)
- Optional, desirable but not required: Hands-on experience with physical vapor deposition techniques, vacuum systems and/or pulsed excimer lasers
- Optional, desired but not required: Experience with machine-learning techniques and data analysis
We offer
Our institution is based on an interdisciplinary, innovative and dynamic collaboration. You will profit from a systematic training on the job, in addition to personal development possibilities and our pronounced vocational training culture. If you wish to optimally combine work and family life or other personal interests, we are able to support you with our modern employment conditions and the on-site infrastructure.
For further information, please contact Dr Nikita Shepelin, email nikita.shepelin@psi.ch or Dr Giovanni Pizzi, email giovanni.pizzi@psi.ch.
Please submit your application online by 16 February 2025 (including addresses of referees) for the position as a PhD Student (index no. 3704-00).
Paul Scherrer Institute, Human Resources Management, Serdal Varol, 5232 Villigen PSI, Switzerland