Requirements: English
Company: Swiss Light Source
Region: Villigen , Argovia
PhD Student in implementation of autonomous epitaxy combining experiments, simulations and machine learning
Join to apply for the PhD Student in implementation of autonomous epitaxy combining experiments, simulations and machine learning role at Swiss Light Source
PhD Student in implementation of autonomous epitaxy combining experiments, simulations and machine learning
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Join to apply for the PhD Student in implementation of autonomous epitaxy combining experiments, simulations and machine learning role at Swiss Light Source
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The Paul Scherrer Institute PSI is the largest research institute for natural and engineering sciences within Switzerland. We perform cutting-edge research in the fields of future technologies, energy and climate, health innovation and fundamentals of nature. By performing fundamental and applied research, we work on sustainable solutions for major challenges facing society, science and economy. PSI is committed to the training of future generations. Therefore, about one quarter of our staff are post-docs, post-graduates or apprentices. Altogether, PSI employs 2300 people.
For the Thin Films and Interfaces Group and the Materials Software and Data Group we are looking for a
PhD Student in implementation of autonomous epitaxy combining experiments, simulations and machine learning
28.04.2025
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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 (it is expected that approximately half of the time during the PhD is spent in the laboratory and half on the software part.) 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
- Masters 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 Ni