Are you eager to work on one of the most potentially impactful and challenging scientific endeavours ever: the development of fusion energy? Do you enjoy collaborating with multidisciplinary teams, have a strong interest in computational physics and machine learning, and want to help accelerate high fidelity turbulence simulations to better understand and optimize future fusion reactors? Then this position might be for you!
While magnetically confined plasma systems offer an opportunity for a clean, safe, and continuous energy source, current fusion experiments are hindered by plasma turbulence that limit the attainable fusion power. Understanding, predicting, and optimizing plasma turbulence is a necessary component for designing reactors and plasma scenarios. The turbulence is well described by direct numerical simulation using the gyrokinetic framework. However, these simulations are too slow for routine optimization and design work. Simplified turbulence models are thus the workhorse tool in the community, but their accuracy is not guaranteed across all plasma regimes. To facilitate both fast and accurate turbulence modelling, this project will apply techniques from AI to construct surrogate operator networks for gyrokinetics, directly solving parts of the equation such as the nonlinear convolutional operator, and accelerating gyrokinetic simulations. The long standing and well-established, validated and verified GENE gyrokinetic framework will be used. An accelerated version of GENE will then be applied for a pertinent physics application, such as understanding and controlling pedestal turbulence.
Besides research you will also contribute to education. Apart from supervising BSc and MSc students in their research projects, other assistance in education, e.g. in bachelor courses, is usually limited to about 5% of your contract time.
This position is enabled by the EAISI Exploratory Multidisciplinary AI Research Program (EMDAIR), through the grant “Neural Green’s Operators (NGO) as Surrogates for Parametric Solutions of Partial Differential Equations (PDE)”, with Dr. Michael Abdelmalik as PI, Dr. Jonathan Citrin and Prof. Guido Huysmans as co-PI, and Drs MJ Pueschel and Daan van Vugt as additional collaborators. EAISI is the Eindhoven Artificial Intelligence Systems Institute. The PhD candidate is expected to spend half of their time in the EAISI building, and be an active part of the AI community at TU/e, by actively participating in the different EAISI research communities, and participating in EAISI events.
 D. Patel et al, Variationally Mimetic Operator Networks, arXiv:2209.12871v2
- A master’s degree (or an equivalent university degree) in Physics, Applied Math, or a related programme.
- Experience in fusion physics is an advantage.
- Experience in computational methods, PDEs, simulation, and/or machine learning is an advantage.
- Knowledge in Python and Fortran an advantage.
- Pro-active and collaborative mentality.
- Eager to learn.
- Prepared to work in an inter-disciplinary environment, combining computer science, machine learning, and fusion physics.
- Good organization and communication skills
- Fluent in spoken and written English.
Conditions of employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
- Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
- Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale 27 (min. €2,541 max. €3,247).
- A year-end bonus of 8.3% and annual vacation pay of 8%.
- High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
- An excellent technical infrastructure, on-campus children's day care and sports facilities.
- An allowance for commuting, working from home and internet costs.
- A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.
Information and application
Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.
Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video.
Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager prof.dr. Roger Jaspers, Science and Technology of Nuclear Fusion, Department of Applied Physics and Education Science, email@example.com, https://www.tue.nl/en/research/research-groups/science-and-technology-of-nuclear-fusion/.
Visit our website for more information about the application process or the conditions of employment. You can also contact HR Services Flux, HRServices.flux[at]tue.nl.
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We invite you to submit a complete application by using the apply button. The application should include a:
- Cover letter in which you describe your motivation and qualifications for the position.
- Curriculum vitae, including a list of your publications/theses and the contact information of two references.
- Brief description of your MSc thesis.
We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.