PhD in Computational and ML-Driven Materials Discovery for Molten Salt Reactors

Molten Salt Reactors (MSRs) are advanced nuclear fission systems that use molten salts as both coolant and fuel carrier. They offer significant advantages, including excellent heat transfer, inherent safety features, and flexible fuel cycles. However, direct contact between radioactive molten salts and structural materials presents serious challenges, particularly in terms of corrosion, hightemperature operation, and radiation-induced degradation. These extreme conditions demand materials with exceptional durability and long-term stability, especially under neutron irradiation and elevated temperatures. Traditional materials development approaches are too slow and narrow in scope to meet these demands. As MSRs gain traction as a promising technology for sustainable energy, there is an urgent need for innovative strategies to accelerate the discovery of durable, high-performance materials tailored to their extreme environments.

This PhD project aims to address these challenges by combining atomistic simulations with advanced machine learning (ML) techniques. By integrating ML, computational materials science, and high-throughput simulation workflows, the project will develop predictive models to efficiently assess and optimize material candidates for corrosion resistance and radiation tolerance. The successful candidate will construct data-driven frameworks to understand salt–alloy interactions, screen novel alloys, and support experimental validation through collaborations with internal and external partners. This research will play a key role in enabling the safe, efficient, and scalable deployment of MSR technologies.

Responsibilities:

1. Perform atomistic simulations (DFT and MD) to investigate degradation mechanisms and stability of advanced alloys under irradiation and in contact with molten salts.
2. Curate, process, and manage simulation data in line with FAIR data principles to ensure quality, reusability, and interoperability.
3. Identify key physical descriptors and structure–property relationships relevant to corrosion and radiation tolerance in candidate alloys.
4. Apply and develop ML models to predict material performance and guide the screening of alloy compositions.
5. Develop and maintain reproducible, automated workflows that integrate simulations, data curation, and ML modelling using appropriate tools and coding best practices.
6. Collaborate with experimental researchers to validate simulation and ML predictions, and interpret results.
7. Supervise BSc/MSc student projects when appropriate.
8. Contribute to dissemination and outreach activities, including participation in conferences and stakeholder meetings.
9. Prepare research presentations and publications, and a PhD thesis within four years.

Requirements:

1. A Master’s degree in computational materials science, physics, chemistry, nuclear engineering, or a closely related discipline.
2. Some experience with atomistic simulation techniques, such as DFT and/or MD, gained through coursework, thesis research, or internships.
3. Familiarity with materials under extreme conditions (e.g. high temperature, irradiation, corrosive environments), especially in the context of energy or nuclear systems.
4. Exposure to machine learning techniques relevant to materials research, or a willingness to develop these skills during the PhD.
5. Programming skills in Python, with interest in applying computational tools to materials modelling.
6. Interest in or prior experience with workflow automation tools or scripting for managing simulation and data analysis tasks.
7. Awareness of FAIR data principles or experience handling simulation data in a reproducible and structured way.
8. Good communication skills and the ability to work effectively in a multidisciplinary and collaborative environment.
9. Proficiency in written and spoken English.

This position is for 1 FTE, will be for a period of 4 years and is graded in pay scale PhD (currently gross € 2.968,--/month in year 1 till gross € 3.801,-- per month in year 4). 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/)

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, chemical energy for the conversion and storage of renewable energy and nuclear fusion – as a clean source of energy.