Group Leader Materials Generation for Energy Self-Driving Labs

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 35 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 Group Leader Materials Generation for Energy Self-Driving Labs

DIFFER’s Chemical Energy Department offers an exciting opportunity to found and lead a new research group at the frontier of energy materials, autonomous experimentation, and Self-Driving Labs (SDLs). We seek an outstanding scientist, either a promising emerging leader or an established leader, who can build a distinctive research programme and contribute directly to the department’s scientific ambitions in energy conversion and storage.

We are building a programme of SDLs for energy conversion and storage, accelerating discovery across related materials, reactions, and processes. The programme automates the full cycle of high-throughput materials generation, characterization, screening, data interpretation, and AI-guided design and experimental planning. The first SDL focuses on electrochemical energy conversion and is already operational, with further capabilities under active development. Additional labs targeting other chemical reactions and materials classes will follow as the programme develops and funding allows.

This is not a conventional materials synthesis position. The new group will develop autonomous materials generation as a core scientific capability for energy SDLs: the methods, experimental strategies, and structured workflows needed to generate test-ready energy-material libraries reproducibly, traceably, and at scale. We use materials generation in a deliberately broad sense: not only synthesis, but the full chain that turns a material concept into a test-ready sample, including sample-format design, sample-library production, formulation, coating and printing, post-treatment, quality control, and sample-to-data provenance.

A central ambition is that the underlying platform logic remains sufficiently general to be retargeted to new energy materials and chemical reactions as the programme grows. Designing generation workflows that are adaptive and suitable for autonomous execution is itself an open research problem, and central to the group’s science. The group’s distinct contribution is the autonomous, high-throughput approach to materials generation; it builds on and complements the Chemical Energy Department’s existing strengths in AI-driven discovery, material characterization, electrochemistry, and plasma chemistry, rather than duplicating them.

DIFFER’s existing materials-generation facilities, including nanoparticle printing and thin-film deposition, offer a concrete starting point, which the group is expected to develop into genuinely high-throughput, autonomous workflows. Beyond these, and where it clearly adds value, we are open to the candidate’s own approach to materials generation. Possible routes include automated wet-chemical synthesis, flow or microreactor synthesis, robotic synthesis and dosing, ink formulation, coating, and thermal post-treatment. Capabilities will be developed where they demonstrably improve throughput, scientific reliability, application relevance, or scientific reach.

Position and requirements

The group leader will own the scientific direction of the autonomous materials-generation research line: which energy-materials questions to pursue, which new strategies to develop, and how self-driving experimentation can reach answers that conventional approaches cannot. Working closely with relevant Chemical Energy research groups and digital and physical SDL development teams, the group leader will define how scientific goals translate into robust, traceable, and AI-compatible workflows, supported by dedicated development and technical teams who handle software and hardware implementation.

The successful candidate will bring a strong experimental profile, a clear SDL-oriented scientific vision, demonstrated or clearly emerging ability to secure external funding, and the capacity to lead multidisciplinary projects. They should have enough hands-on experimental depth to lead credible autonomous materials-generation research, but they need not be defined by a single synthesis technique. The scope outlined here marks a starting point, and we expect the group leader to shape it through their own scientific vision, methods, and collaborations, in alignment with the Chemical Energy Department.

Responsibilities:
1. Found and lead a research group in autonomous materials generation for energy SDLs, setting its scientific direction, building its international visibility, and developing a distinctive research agenda aligned with the Chemical Energy Department.
2. Develop high-throughput, autonomous strategies for generating scientifically reliable and reproducible energy-material libraries.
3. Define the experimental decision logic of self-driving materials-generation workflows by translating scientific questions into structured protocols, including experimental variables, decision points, and go/no-go criteria for autonomous execution.
4. Define the scientific requirements for trustworthy self-driving experiments, including traceability, sample-to-data provenance, data quality, reproducibility standards, and safe experimental practice, working with technical and SDL development teams responsible for implementation.
5. Scientifically lead the materials-generation component of integrated SDL campaigns, ensuring robust interfaces with AI-guided materials design, characterization, and application-relevant testing.
6. Establish an evidence-based scientific roadmap for future materials-generation capabilities, including synthesis and sample-preparation approaches, and provide clear scientific justification for major capability developments.
7. Build collaborations with academic, industrial, and deep-tech partners to advance autonomous materials generation, including workflow development, advanced robotics for materials generation, and instrument co-development where scientifically appropriate.
8. Secure external funding; recruit, supervise, and mentor researchers; publish high-impact work; and champion FAIR experimental practices.

Requirements:
1. PhD in experimental materials science, chemistry, chemical or process engineering, applied physics, mechanical engineering, or a related experimental discipline, with a research focus on energy or functional materials.
2. A track record, appropriate to career stage, of research publications and external funding, with evidence of independent scientific leadership or a clear trajectory toward it.
3. A collaborative, programme-oriented mindset and the ability to formulate an independent research agenda in autonomous materials generation that advances the Chemical Energy Department's broader ambitions in SDLs for energy conversion and storage.
4. Scientific and experimental depth in at least one relevant materials-generation route, such as nanoparticle printing, thin-film deposition, automated wet chemistry, flow or microreactor synthesis, robotic synthesis or dosing, ink formulation, coating, or thermal post-treatment.
5. Demonstrated experience with high-throughput, automated, or semi-autonomous experimental workflows, and the ability to translate hands-on materials-generation expertise into autonomous, reproducible, and scientifically reliable SDL workflows.
6. A clear scientific vision for closed-loop self-driving experimentation, including how autonomous workflows can be made reproducible, traceable, adaptive, and scientifically meaningful.
7. Working knowledge of design of experiments, active learning, Bayesian optimisation, or related closed-loop planning concepts, sufficient to collaborate effectively with AI, data-science, and SDL development teams, without owning AI-method or middleware development.
8. Ability to connect materials-generation workflows to relevant screening, characterization, and application-relevant testing, initially including electrochemical performance and, where relevant, in-situ or in-line approaches.

Terms and conditions

Information and application

To apply for this position, please click the button underneath and submit your application via the DIFFER online portal. Your application should include the following documents:

1. CV with full publication list and contact details for two references.
2. One PDF that combines a cover letter, maximum two pages, describing your motivation and fit, and a research plan, maximum four pages, outlining your vision for your research group at DIFFER.

Please note: Only complete applications submitted through the online portal will be considered. Applications sent by email will not beaccepted.

Closing date: September 13, 2026.
Interview planning: first (online) interviews are 22, 24 and 28 September 2026, and second (on-site) interviews are 12 and 14 October 2026.

Closing date
September 13, 2026