Postdoc modeling transport in the plasma edge (x/f/m)

Postdoc modeling transport in the plasma edge (x/f/m)

Published Deadline Location
11 Jun 30 Jun Eindhoven
Interested to work as a Postdoc at DIFFER on conducting edge simulations for reactor design and improving fast edge transport model surrogates applicable to fusion reactors? Read further

Job description

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.

Specifications

DIFFER

Requirements

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

Conditions of employment

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/)

Employer

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.

Additional information

For more information concerning the position please contact Sven Wiesen via s.wiesen@differ.nl. To apply for this position, please click the button underneath:

Specifications

  • Postdoc
  • Natural sciences
  • max. 40 hours per week
  • Doctorate
  • 3386

Location

De Zaale 20, 5612AJ, Eindhoven

View on Google Maps

Interesting for you

X

Apply for this job

Apply for this job

This application process is managed by the employer (DIFFER). Please contact the employer for questions regarding your application.

Thank you for applying

Please contact the employer for questions regarding your application.

Tip: save this job as favorite in your AcademicTransfer account. This gives you an immediate overview and makes it easy to find the job later on. No account yet? Create it now and take advantage of other useful functionalities too!

Application procedure

Application procedure

Make sure to apply no later than 30 Jun 2024 23:59 (Europe/Amsterdam).