PhD Position: Agentic AI-Driven Experiments for Materials Discovery

DIFFER

DIFFER: Science for future energy

At the Dutch Institute for Fundamental Energy Research (DIFFER) we work on a future in which clean energy will be available to everybody, anywhere in the world. DIFFER’s mission is to perform leading fundamental research on materials, processes, and systems for a global sustainable energy infrastructure.

Our research focuses on two major energy themes: fusion energy as a clean, safe and sustainable energy source and chemical energy. We work in close partnership with (inter)national academia and industry. DIFFER is one of the ten research institutes of the Dutch Research Council (NWO).

Within our institute physicists, chemists, engineers, and other specialists work together in multidisciplinary teams to accelerate the transition to a sustainable society. DIFFER’s workforce is currently composed of ~160 scientists (of which 60 guests and interns), supported by ~40 technicians and ~40 support staff members.

The global nature of the energy challenge is apparent from the international representation of our employees, who originate from over 30 different countries. To strengthen our commitment to diversity, we formed a task force to design, implement, and monitor diversity and gender equality initiatives. 
 

Differ is looking for a

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.

Position and requirements

In this position, you will have the following responsibilities:

• Design, implement, test and operate an agentic AI-driven experimental workflow that translates AI-proposed compositions into synthesis and characterization plans, supporting quality control, traceability and structured reporting.
• Carry out high-throughput synthesis of material libraries with a nanoparticle printer, with systematic control of composition and deposition conditions.
• Characterize samples with micro-XRF composition mapping and SEM, and develop reproducible Python pipelines to extract composition, homogeneity and morphology metrics.
• Develop and maintain a structured, FAIR-aligned dataset of successful, partial and negative outcomes, including realizability labels, and use it to support AI model development.
• Connect the AI agents to the project's data, planning and reporting steps, including databases and analysis tools, so that the path from candidate selection to validated results is traceable and reproducible. 6.Write well-tested, reproducible research software, contribute to publications and conferences, and complete and defend a PhD thesis within four years.

To fulfill the responsibilities listed above, we are looking for a candidate who meets the following requirements:

• A Master's degree in computer science, artificial intelligence, data science, physics, chemistry, materials science, or a related field.
• Strong Python skills and good software-engineering practice, including Git, testing and reproducible code.
• Experience or a strong interest with agentic AI systems, for example LLM-based agents with tool use, workflow automation, or integration with databases and data pipelines.
• Motivation to apply AI in a laboratory setting and willingness to learn hands-on materials synthesis and characterisation. Prior experience with laboratory instrumentation (e.g. nanoparticle printer, micro-XRF, SEM) is not required.
• Experience with laboratory automation, instrument control, or image and spectral data analysis is an advantage.
• A hands-on, meticulous working style, good command of English, and the ability to work effectively in a multidisciplinary team.

Terms and conditions

This position is for 1 FTE, will be for a period of 4 years and is graded in pay scale 19. The starting salary is €3.115,- increasing to €3.989,- in the fourth year of the PhD position. The expected starting date is January 1, 2027.

The position will be based at DIFFER (www.differ.nl), Eindhoven, The Netherlands. When fulfilling a position at DIFFER, you will have an employee status at NWO. You can participate in all the employee benefits NWO offers. We have a number of regulations that support employees in finding a good work-life balance. At DIFFER we believe that a workforce diverse in gender, age and cultural background is key to performing excellent research. We therefore strongly encourage everyone to apply. 

More information on working at NWO can be found at the NWO website (https://www.nwo-i.nl/en/working-at-nwo-i/jobsatnwoi/). 

Information and application

To apply, please submit your application via the website.

Your application should include the following documents: 

• A cover letter explaining your motivation and suitability for the position.
• A CV, including a list of publications, if applicable.
• Transcripts of your Master’s and Bachelor’s course grades. 

Please note: only complete applications submitted through the website will be considered. 

Applications sent by email will not be accepted.