A significant challenge in energy materials research is developing autonomous AI-driven systems that enhance discovery by improving research speed, precision, and reproducibility. At DIFFER, in collaboration with our partners, we are developing Self-Driving Laboratories (SDLs) for energy materials, processes, and system design. These SDLs combine machine learning, optimization algorithms, and digital tools to create robust infrastructures that streamline laboratory workflows, uncover insights, and enable efficient, automated experimentation.

The objective of this PhD project is to design and develop a Digital Twin (DT) that simulates increasingly autonomous physical SDL environments. The DT will combine discrete event simulation, process optimization, and advanced visualization tools to replicate lab workflows, analyze processes, and identify areas for improvement. It will optimize lab performance by identifying efficient configurations, addressing bottlenecks, and detecting anomalies or hazards. The project will involve developing simulation models, implementing optimization algorithms, and building visualization tools. Additionally, the PhD candidate will contribute to lab orchestration by integrating robotics, AI-driven decision-making, and high-throughput experimentation.

RESPONSIBILITIES

1. Analyze physical lab devices and data to optimize workflows.
2. Develop discrete event simulations to replicate and analyze lab processes.
3. Build software tools to visualize simulation outcomes.
4. Implement optimization algorithms to improve lab performance and efficiency.
5. Design a DT framework integrating lab orchestration, robotics, AI, and high-throughput experimentation.
6. Collaborate with consortium researchers, supervise junior researchers' projects.
7. Prepare research presentations and publications, and complete a PhD thesis within four years.

REQUIREMENTS

1. Master’s degree in Computer Science, ML, AI, or a closely related field.
2. Strong expertise and hands-on experience in developing optimization algorithms.
3. Demonstrated software development skills, supported by project repositories.
4. Proficiency in Python programming.
5. (Preferred) Experience in simulation programming, such as SimPy.
6. (Preferred) Experience with ML techniques, especially reinforcement learning (RL).
7. Good communication skills and a collaborative mindset.
8. Excellent written and verbal communication skills in English.

This position is for 1 FTE, will be for a period of {link time} and is graded in pay scale PhD. The starting salary is € 2968,- in the first year and will increase to € 3801,- per month in the fourth year of the employment. The position will be based at DIFFER (www.differ.nl) and the working location will be at TU Eindhoven. 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/)

Dutch Institute for Fundamental Energy Research

The Dutch Institute for Fundamental Energy Research (DIFFER) performs leading fundamental research on materials, processes, and systems for a global sustainable energy infrastructure. We work in close partnership with (inter)national academia and industry. Our user facilities are open to industry and university researchers. As an institute of the Dutch Research Council (NWO) DIFFER plays a key role in fundamental research for the energy transition.

We use a multidisciplinary approach applicable on two key areas, solar fuels for the conversion and storage of renewable energy and nuclear fusion – as a clean source of energy.

For further information about the position please contact: Dr. Süleyman Er, s.er@differ.nl

Please submit your application via our online portal via the button underneath, including a Cover Letter and CV.
Applications that are not submitted via our online portal will not be accepted.

Closing date
Jan 31, 2025