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, develop, and validate machine learning (ML) models to accelerate energy materials discovery in increasingly autonomous Self-Driving Laboratory (SDL) environments. The focus will be on applying techniques such as Bayesian Optimization (BO), reinforcement learning (RL), and causal learning (CL) to optimize experimental planning, improve workflows, and uncover cause-and-effect relationships. The PhD candidate will ensure model validation and enhance explainability through interpretable techniques, such as visualizations and counterfactual analysis.

RESPONSIBILITIES

1. Analyze experimental and simulation data to extract meaningful insights.
2. Develop and apply ML models, including BO, RL, and CL, for experimental planning, optimization, and causal analysis.
3. Ensure model validation and explainability using interpretable techniques.
4. Collaborate with consortium researchers, supervise junior researchers' projects.
5. 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. Proven ML experience through projects, research, or competitions.
3. Practical experience with ML/DL libraries such as scikit-learn, TensorFlow, or PyTorch.
4. Proficiency in Python programming.
5. (Preferred) Experience in BO, RL, CL, computer vision, or signal processing.
6. (Preferred) Experience with generative modeling techniques.
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 4 years and is graded in pay scale PhD. The starting salary is € 2968,- in the first year and will increase to € 3801,- 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