PhD Position: Agentic AI-Driven Experiments for Materials Discovery

PhD candidate who will join the Autonomous Energy Materials Discovery (AMD) group at DIFFER, working with researchers and engineers who are developing autonomous laboratory technologies.

The project is carried out in close collaboration with an industrial partner developing generative AI for materials discovery. The position suits a candidate with strong programming skills, from computer science, AI, physics, chemistry or materials science, who wants to work at the interface of agentic AI, laboratory automation and materials science, and who is happy to spend a substantial part of the project in the lab.

Roughly one third of your time will go to the digital workflow and two thirds to experimental work. Training and technical support are provided for all instruments. AI methods can propose material candidates far faster than they can be synthesized and validated. You will help close this gap by building an agentic AI workflow that turns AI-proposed alloy compositions into executable synthesis and characterization plans, and by generating the experimental evidence that shows which candidates can actually be made. You will produce material libraries with a high-throughput nanoparticle printer and characterise them with micro-XRF and SEM. Successful, partial and failed outcomes will be captured in a structured, machine readable dataset to support AI model development and give feedback on material realizability.