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- Aufgaben: Develop and validate autonomous growth optimization for oxide thin films using experiments and machine learning.
- Arbeitgeber: Join the Paul Scherrer Institute, Switzerland's largest research hub for natural and engineering sciences.
- Mitarbeitervorteile: Enjoy a dynamic work environment with personal development opportunities and modern employment conditions.
- Warum dieser Job: Be part of cutting-edge research that tackles major societal challenges while earning your PhD at EPFL.
- Gewünschte Qualifikationen: Master’s in Physics, Chemistry, Engineering, or Materials Science; strong programming skills required.
- Andere Informationen: Female candidates are strongly encouraged to apply; hands-on experience is a plus but not mandatory.
Das voraussichtliche Gehalt liegt zwischen 36000 - 60000 € pro Jahr.
PhD Student in implementation of autonomous epitaxy combining experiments, simulations and machine learning
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.
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 or Dr Giovanni Pizzi, email
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
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PhD Student in implementation of autonomous epitaxy combining experiments, simulations and mach[...] Arbeitgeber: Paul Scherrer Institut
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Paul Scherrer Institut HR Team
StudySmarter Bewerbungstipps 🤫
So bekommst du den Job: PhD Student in implementation of autonomous epitaxy combining experiments, simulations and mach[...]
✨Tip Number 1
Familiarize yourself with the latest advancements in autonomous epitaxy and physical vapor deposition techniques. This knowledge will not only help you during the interview but also demonstrate your genuine interest in the field.
✨Tip Number 2
Engage with current research and publications related to materials science and machine learning. Being able to discuss recent findings or methodologies can set you apart from other candidates.
✨Tip Number 3
Network with professionals in the field, especially those who have experience in programming for scientific applications. They can provide insights and potentially refer you to opportunities within the Paul Scherrer Institute.
✨Tip Number 4
Prepare to showcase your programming skills, particularly in Python. Consider working on a small project that demonstrates your ability to combine programming with materials science concepts, as this will be highly relevant to the role.
Diese Fähigkeiten machen dich zur top Bewerber*in für die Stelle: PhD Student in implementation of autonomous epitaxy combining experiments, simulations and mach[...]
Tipps für deine Bewerbung 🫡
Understand the Role: Make sure to thoroughly read the job description and understand the specific requirements for the PhD position. Highlight your relevant experience in physical sciences, engineering, and programming in your application.
Tailor Your CV: Customize your CV to emphasize your educational background, particularly your Master’s degree in Physics, Chemistry, Engineering, or Materials Science. Include any relevant projects or research that showcase your programming skills and experience with physical vapor deposition techniques.
Craft a Strong Cover Letter: Write a compelling cover letter that reflects your passion for materials science and automation. Discuss your motivation for applying to the Paul Scherrer Institute and how your skills align with the goals of the Thin Films and Interfaces Group.
Highlight Teamwork and Communication Skills: In your application, emphasize your ability to work in teams and your excellent communication skills in English. If you have any experience working collaboratively on research projects, be sure to mention it.
Wie du dich auf ein Vorstellungsgespräch bei Paul Scherrer Institut vorbereitest
✨Show Your Passion for Materials Science
Make sure to express your enthusiasm for materials science and discovery during the interview. Share specific examples of projects or experiences that ignited your interest in this field, as it will demonstrate your motivation and commitment.
✨Highlight Your Programming Skills
Since strong programming skills are crucial for this role, be prepared to discuss your experience with Python and any other programming languages you know. Consider sharing a project where you applied these skills, especially if it relates to automation or data analysis.
✨Discuss Teamwork Experience
The position requires excellent teamwork abilities. Be ready to provide examples of how you've successfully collaborated with others in past projects. Highlight your communication skills and how you contribute to a positive team dynamic.
✨Prepare Questions About the Research
Demonstrate your interest in the Paul Scherrer Institute and the specific research groups by preparing thoughtful questions. Inquire about ongoing projects, the integration of machine learning in their work, or the potential impact of your research on future technologies.
