We are looking for a PhD candidate who is interested in AI-Driven Digital Twins for Energy Conversion Systems!
Location
De Zaale 20 Eindhoven
Department
Theme Solar Fuels
Group
Autonomous Energy Materials Discovery
Kind of contract
Temporary
Kind of function
PhD student
Publication date
June 22, 2026
Closing date
July 12, 2026
DIFFER
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 is interested in AI-Driven Digital Twins for Energy Conversion Systems!
The PhD project focuses on the development and validation of the AI-enabled Digital Twin at the heart of the Horizon Europe AIM project. Over four years, you will build a modular framework that couples atomistic chemistry, mesoscale transport, and device-level performance into a continuously learning model of solar-driven energy conversion devices. The position covers the full life cycle of the Digital Twin, from architecture design and machine-learning calibration to validation against experimental data.
This job offer is part of the Horizon Europe project AIM. AIM (AI-Multiscale Integration for Waste-to-Value Digital Twins) is a four-year project, funded by the EU through Horizon Europe. It brings together seven research groups and companies across Europe to develop advanced computational tools for turning waste (such as hard-to-recycle plastics, flue gases, and brine) into valuable fuels, chemicals, and materials using solar-powered devices. The core challenge is simulating the chemistry of these processes at very different levels, from individual atoms up to full working devices. These scales are hard to connect to one another. AIM uses artificial intelligence and machine learning to bridge that gap and build a Digital Twin that links atomic-scale chemistry, mesoscale transport, and device-level performance, allowing researchers to test, predict, and optimize designs in a computer before building them physically. By improving the accuracy and speed of these simulations, automating the workflows, and validating the models against real experimental data, the project aims to accelerate the development of cleaner, more efficient technologies for a circular economy while training the next generation of researchers in these digital skills.
The project is supported by the European Innovation Council and its members. This project has received funding from the European Innovation Council and the European Union’s Horizon Europe research and innovation programme under Grant Agreement No.101306664.
Position and requirements
The position is embedded in the Autonomous Energy Materials Discovery Group within the Chemical Energy Department of DIFFER. Our group develops and applies computational and AI methods to accelerate the discovery of molecules and materials for energy conversion and storage. Within AIM, DIFFER contributes expertise in AI-driven materials modelling, digital research workflows, and multiscale integration for Waste-to-Value energy conversion systems. As a PhD in AIM, you will help build the first ab initio–based Digital Twin for these devices, working alongside leading European research groups and with access to national high-performance computing. The project also pioneers the use of agentic AI and large language models to orchestrate complex scientific workflows.
Responsibilities:
1. Design and implement the core architecture of the AIM Digital Twin, linking atomistic, mesoscale, and device-scale models on the project’s FAIR data and workflow platform.
2. Develop machine-learning surrogate models for model calibration, uncertainty quantification, and active learning to accelerate multiscale simulations.
3. Integrate validated models from partner groups (electronic-structure, photo-/electrocatalysis, multiscale transport) and benchmark the Digital Twin against experimental data provided by project partners across device classes.
4. Collaborate closely with the AIM consortium and present results at consortium and portfolio meetings.
5. Contribute to open-source software, FAIR data management, scientific publications, and presentations at international conferences.
6. Contribute to the supervision of junior student projects, where appropriate.
7. Complete and defend a PhD thesis within four years.
To get started on this PhD position, it is important that the candidate has:
1. A Master’s degree in chemistry, physics, materials science, computational science, chemical engineering, or a related field.
2. Solid scientific programming skills in Python, with an interest in writing clean, reproducible, and tested code (e.g. using Git and modern workflow tools).
3. An aptitude for working at the interface between methods and scales, connecting different models, codes, and data sources, rather than specialising in a single technique.
4. A strong motivation to apply AI and simulation to sustainable-energy and clean-technology challenges.
5. Hands-on experience with one or more of DFT, molecular dynamics, multiscale/materials modelling, or machine learning for the sciences including, Bayesian methods, uncertainty quantification, scientific AI workflows, automated discovery pipelines, or FAIR data/workflow platforms, is an advantage.
6. Strong analytical and problem-solving skills, with the ability to work effectively in an international and multidisciplinary research environment.
7. Good command of written and spoken English.
Terms and conditions
This position is for 1 FTE, will be for a period of four years and is graded in pay scale PhD; the starting salary is €3.115,- increasing to €3.989,- in the fourth year of the PhD position. The expected starting date is October 1 2026 and we expect that the interviews will take place in the first week of August. The closing date of the vacancy is July 12 2026.
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/)
Information and application
For more information concerning the position please contact Lotte Wildhagen via
[email protected].
Closing date
July 12, 2026
Apply now
Go to the Vacancies page.
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