State-of-the-art simulations of transport effects in the fusion plasma edge has become an import element in the prediction of reactor-scale operational scenarios providing compatibility to both, required heat and particle exhaust constraints and good fusion plasma core performance. Given the multi-scale multi-physics nature of the problem, solutions for a reactor relevant operational regime are hard to achieve given slow numerical convergence rates. An employment of numerical tools, that e.g., allow to predict relevant dynamics for plasma control by using an integrated approach with suitable fidelity, are not yet existing. In very recent years, the fusion community has started to develop fast surrogate models based on Machine Learning / AI models to speed up significantly the employed tools. Such tools have demonstrated to be generally applicable and to be fast; their predictive capability however is a trade-off balancing speed and level of fidelity.

DIFFER is seeking to hire a post-doctoral researcher for approximately two years to work with the Integrated Modeling group at the institute within various frameworks (EUROfusion and other private partners in fusion) to conduct edge simulations for reactor design and improve fast edge transport model surrogates applicable to fusion reactors.

Responsibilities and tasks:

• Employment of state-of-the-art tools to run plasma edge simulations
• Development of ML/AI based fast surrogate models at increased fidelity, e.g. inclusion of a hierarchy of neutral physics and plasma drift flow effects in (partially existing) surrogates
• Deploy the resultant improved and fast models and coupling of fast edge model e.g. to core plasma simulators
• Uncertainty Quantification of assumed model parameters
• Employment of reactor scale simulations

Required skills:

• Good understanding of fusion edge plasmas transport physics
• Experience with transport solvers (i.e. SOLPS-ITER)
• Good knowledge of ML/AI based techniques to develop fast surrogates (deep neutral networks) and capability to develop own efficient model learning schemes (deep learning techniques, representation learning, active learning, etc)
• (optional) Experience with dynamic simulations in the plasma edge

This position is for 1 FTE, will be for a period of 2 years 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/)

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.