The new group will broadly leverage DIFFER's state-of-the-art infrastructure, including material synthesis platforms (VSParticle Nanoparticle Printer, Pulsed Laser Deposition), advanced analytical instrumentation (XRD, SEM, TEM, XRF, XPS), and automated chemical characterization tools (electrochemical workstations, ICP-MS, GC-MS), to enable transformative advancements in autonomous materials discovery.
The successful candidate will lead the development of integrated frameworks for automating a variety of experimental workflows. You will focus on connecting relevant laboratory setups and experimental design with advanced AI-driven methods to enable automation in electro-, thermo-, or plasma-driven energy carrier conversion experiments.
The Group Leader is expected to create pioneering solutions to seamlessly integrate diverse laboratory equipment and robotics, establishing fully automated experimental workflows, and implementing AI-assisted adaptive experiment planning supported by real-time data analytics. This role involves collaboration with researchers and engineers for the design of robust, FAIR-compliant data infrastructures capable of handling large-scale, real-time experimental data. The Group Leader will also play a central role in translating insights from digital twin simulations, currently under development at DIFFER, into the design and optimization of real-world experimental workflows.
We seek an outstanding scientist or engineer experienced in building automated laboratories with integrated lab equipment and handling complex experimental workflows. The ideal candidate has demonstrated experience integrating laboratory automation, machine learning-driven optimization, and high-throughput experimentation in materials science, chemistry, physics, or related fields, and has contributed to collaborative projects at the forefront of AI-assisted experimentation.
Responsibilities: - Lead the establishment and management of an integrated autonomous experimentation platform, combining advanced synthesis, material and chemical characterization equipment into a unified, automated system.
- Develop and implement AI-driven methodologies for autonomous, adaptive experimentation – including active learning algorithms and self-optimizing experimental workflows – ensuring effective integration across heterogeneous instruments.
- Contribute to the development of a robust data infrastructure adhering to FAIR data principles, enabling real-time data acquisition, analysis, and decision-making to accelerate discovery.
- Apply insights from digital twin simulations developed by colleagues to improve the design of physical laboratory setups and experimental workflows.
- Collaborate closely with materials scientists, chemists, and engineers to ensure effective integration of synthesis and characterization techniques, and AI tools into the self-driving lab framework.
- Secure external research funding through competitive grants and industrial partnerships to expand the capabilities and impact of the group’s research.
- Supervise PhD students, postdoctoral researchers, and engineers, promoting scientific excellence and career development.
- Disseminate research findings through publications, presentations, and stakeholder engagement activities.