PhD Student

Last application date
Sep 30, 2020 23:59
TW17 - Department of Applied Physics
Employment category
Doctoral fellow
Limited duration
Master in Engineering Physics, Physics, Mathematics, Computer Science or Electrical Engineering
Occupancy rate
Vacancy Type
Research staff

Job description

The Research Unit Nuclear Fusion (infusion) of the Department of Applied Physics at Ghent University is looking for a PhD student in the field of fusion data science. This research area covers the development and application of modern data science methods in the context of nuclear fusion science and technology. We are a relatively young and small research group, working on a broad range of applications of Bayesian inference, machine learning and information geometry in nuclear fusion science. Prospective applicants are invited to consult our website for an overview of our current research topics.


The research on controlled nuclear fusion is a large-scale international endeavor aimed at developing fusion power as a clean and limitless source of energy. Fusion is a very interdisciplinary field, involving the rich physics of magnetized plasmas, as well as the technology of fusion devices. The permeation in fusion of the methods of Bayesian probability and machine learning is a relatively new development, resulting in a multitude of possible applications and challenges.

Our research unit not only focuses on applications of existing data science techniques in fusion, but also develops new methods by building on the foundations of Bayesian probability, information theory and the mathematical field of information geometry. In addition, by incorporating physical constraints into our techniques, we aim to narrow the gap between first principles theoretical or modeling approaches of plasma phenomena and purely data-driven techniques.


The research topic of the present PhD vacancy is related to pattern recognition in spaces of probability distributions. The main idea is to model various sources of uncertainty and stochasticity of plasma quantities by probability distributions (e.g. time scales of heat transport, properties of plasma instabilities and plasma turbulence), and to characterize the dependence of such distributions on plasma conditions. The distributions are regarded as points in probability spaces, described within the framework of information geometry. This enables new and powerful techniques for classification and regression, in order to clarify the dependence of complex phenomena in fusion plasmas on specific machine parameters. Depending on the background and interests of the PhD candidate, the focus of the research can lie on development and implementation of new techniques, on the physics of the investigated plasma phenomena, or on new scenarios for plasma control.


We offer the following:

  • A PhD position for 1 year, which in case of positive annual evaluations, will be extended up to a maximum of 4 years, with a view to obtaining the PhD in Engineering Physics.
  • An exciting research environment at the intersection of theory and application in the multi-disciplinary research domain of fusion data science.
  • The possibility to participate in international conferences and collaborations.

Profile of the candidate

The field of fusion data science is situated at the interface between several research disciplines, involving probability theory, machine learning, information theory and plasma physics. We are therefore looking for a highly motivated candidate who is prepared to take on a challenge in a relatively new but exciting research domain. As the precise research topic will be determined in consultation with the PhD candidate, the job profile is relatively flexible. Here are a few different possibilities:

  • You have a physics background (plasma physics is a plus), combined with a strong interest in probability and machine learning. You would like to work on data science applications in a physics domain, but you are also willing to further the physics understanding of fusion plasmas using data science methods.


  • You are well-versed in modern machine learning techniques. With your broad and deep understanding of the various methods, your skills go beyond the mere calling of routines in standard software libraries. In addition, you are prepared to invest some time in learning the necessities of fusion plasma physics or fusion technology, allowing you to apply domain knowledge in your machine learning solutions.


  • You are a mathematician looking to make a tangible contribution to an applied research field. You are interested in further development of the mathematical foundations underlying the work of the research unit, but you do not shy away from practical implementation of your work in computer codes.


Creativity and a good sense of initiative are highly desired in this position. Furthermore, you will collaborate within the European and international fusion community. This may involve travel abroad to some of the international fusion laboratories that we collaborate with.

How to apply

Please submit your application by email to Prof. Dr. Geert Verdoolaege () by September 30, 2020, 23:59 CEST. The application must consist of the following files:


  •  Your CV, named as “CV_[full_name]”
  •  Motivation letter, named as “Motivation_[full_name]”
  •  Diploma and all transcripts of records (BSc and MSc), named as “Diploma_transcripts_[full_name]”


Only complete application files will be considered. Please contact in case of questions.