Postdoctoral Researcher in the Development of Thin Films and Deposition Processes using Artificial Intelligence and Autonomous Experiments

1000scholars; Dübendorf; On-site; CHF 90,000 - 120,000; Full time

Job summary

Empa invites applications for a Postdoctoral Researcher position focused on developing thin films and deposition processes using AI and autonomous experiments. Based in Dübendorf, you will operate at the interface of experimental materials science and materials informatics, collaborating within an interdisciplinary team.

You will conduct PVD experiments, utilize high-throughput workflows, and apply AI/ML to optimize processes and materials properties, contributing to next‑generation devices and

Qualifications

• PhD in physics, materials science, chemistry, electrical engineering or related discipline.
• Knowledge of UHV and thin-film technology is preferred.
• Experience with PVD and surface science is desired.

Responsibilities

• Conduct physical vapor deposition experiments using advanced PVD processes such as synchronized HiPIMS.
• Perform in-depth materials characterization and surface analysis.
• Apply and improve high-throughput experimental workflows including closed-loop thin-film optimization.
• Apply AI and Machine Learning for data analysis and modelling.
• Develop, improve and implement HW/SW concepts to automate experiments and enable autonomous experiments.

Skills

Python programming

Lab automation

AI / machine learning

PVD

Surface analysis

English communication

Education

PhD in physics, materials science, chemistry, electrical engineering or related discipline

Tools

HiPIMS

RHEED

AES

XPS

UHV systems

Sputtering

Job description

Postdoctoral Researcher in the Development of Thin Films and Deposition Processes using Artificial Intelligence and Autonomous Experiments

Empa - Swiss Federal Laboratories for Materials Science and Technology

Dübendorf, Switzerland

The opportunity

Contribute to groundbreaking research on accelerated thin-film and process development for emerging applications such as ferroelectric memory, photonic integrated circuits and next-generation telecommunications. You'll work at the exciting intersection of experimental materials science and materials informatics, collaborating in an interdisciplinary team.

Your tasks

• Conduct physical vapor deposition experiments using advanced PVD processes such as synchronized HiPIMS
• Perform in-depth materials characterization and surface analysis
• Apply and improve high-throughput experimental workflows including closed-loop thin-film optimization
• Apply AI and Machine Learning for data analysis and modelling
• Develop, improve and implement HW/SW concepts and components to automate experiments and enable autonomous experiments

The focus of this position is to use advanced PVD processes to develop state-of-the art thin films, such as piezoelectric and ferroelectric nitrides. You will use synchronized HiPIMS processes to tune the properties and performance of the films. The experiments will be informed by in-situ surface and interface analysis.

Your work will make use of our group's unique comprehensive infrastructure for materials and device characterization. In addition, the work will benefit from a unique self-driving thin-film laboratory. This laboratory is currently in development and will include sputter deposition and in-situ surface analysis techniques such as RHEED, AES and XPS. Part of the work is also to optimize the workflows in this laboratory in collaboration with the team.

Your profile

• PhD degree in physics, materials science, chemistry, electrical engineering or a related discipline
• Knowledge of UHV and thin-film technology
• Prior experience in PVD and surface science
• Prior experience with piezoelectric- or ferroelectric nitrides is an advantage
• Knowledge of PVD process development and pulsed sputtering is an advantage
• Proficiency in Python programming
• Interest in lab-automation and working with agentic AI (e.g. Claude Code, Codex)
• Excellent communication skills in English
• Strong academic track-record and publication history
• The ability to collaborate in a multidisciplinary research environment is essential. Personal initiative, ability to work systematically, reliability, responsibility, teamwork and communication skills are a must.