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.
DIFFER IS LOOKING FOR A POSTDOC ON FUSION MATERIALS MODELLING (X/F/M)
At the Autonomous Energy Materials Discovery [AMD] Research Group of DIFFER, we develop and use automated virtual materials discovery frameworks – powered by high-throughput physics-based classical and quantum calculations, artificial intelligence methods, and advanced data-infrastructures – to accelerate the discovery of molecules/materials for energy applications.
This position is for candidates who want apply their experience in first-principles modelling for the design of materials for nuclear fusion reactors. The project involves density functional theory (DFT) and molecular dynamics (MD) simulations of W-based fusion wall materials under extreme conditions. In addition, this position will entail generation of machine-learned (ML) interatomic models and their use to elucidate the experimentally observed structural and physicochemical features of the wall materials.
POSITION AND REQUIREMENTS
- Perform DFT and MD calculations on W-based materials for nuclear fusion reactor walls.
- Analyze calculation data to generate new insights on radiation-induced point defects in W.
- Collaborate with experimental researchers for the development of new fusion wall materials.
- Prepare research publications and presentations.
- PhD in Computational Physics/Chemistry/Materials Science.
- Experience in DFT calculations on condensed matter, as evident by publications.
- Experience in MD simulations using on-the-fly machine learning force fields.
- Working knowledge of electronic structure codes, such as VASP or similar.
- Good knowledge of Python.
- Effective written and verbal communication skills in English.
TERMS AND CONDITIONS
This position is for 1 FTE, will be for a period of 1 year with possibility of extension and is graded in pay scale 10. 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 Suleyman Er via firstname.lastname@example.org. To apply for this position, please click the button underneath:
May 31, 2023