Full-time position as professor in the rank of assistant professor (tenure track) in the discipline of artificial intelligence/big data science/mathematical modelling

Last application date
May 25, 2016 23:59
WE02 - Department of Applied Mathematics, Computer Science and Statistics
Occupancy rate
Vacancy Type
Autonomous academic staff

The faculty of Sciences has a vacancy for a professorship, starting from October 1st 2016. It concerns a full time position as Professor in the rank of Assistant Professor tenure track in the Department of Applied Mathematics, Computer Science and Statistics, charged with academic teaching, academic research and carrying out academic services in one of the following disciplines:

  • of artificial intelligence. The research field of artificial intelligence is interdisciplinary by nature and a number of scientific disciplines meet in this field, including computer science, mathematics, statistics, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields. We expect the research of the candidate to be deeply rooted in a fundamental and computational approach to artificial intelligence, with leads into other perspectives and application domains of artificial intelligence. The ideal candidate has therefore a pronounced computer science profile in the world of artificial intelligence research.
  • big data science. The research field of Big Data Science is interdisciplinary by nature at the intersection of a number of scientific disciplines, including the more technical disciplines of computer science, mathematics and statistics on the one hand and important application domains such as psychology, geography, biology, medicine, neuroscience and environmental science on the other hand. We expect the research of the candidate to be deeply rooted in a fundamental and computational approach to Big Data Science, with links to complementary approaches and application domains of Big Data Science. The ideal candidate has therefore a pronounced computer science profile in the world of Big Data Science research.
  • mathematical modelling. The selected applicant will have an expertise that is complementary to ongoing research in the department and will reinforce the department’s scientific potential and strengthen the research activities in the abovementioned field. The applicant will focus on fundamental research and investigate mathematical problems, implementation problems and simulation techniques related to modelling.

He/she is also expected to realize a substantial research output of high quality, to propose subjects for master and PhD theses and to apply for research grants. The applicant will be charged with teaching courses at bachelor and master level.


  • a PhD with dissertation: computer science or mathematics or a field that is declared admissible by the evaluation committee; or a degree recognized as equivalent;
  • at least two years of postdoctoral experience as of 1 October 2016;
  • an outstanding research record in the field of study concerned, demonstrated by recent publications in international peer reviewed journals and/or books;
  • for the discipline big data science: international publications that reflect a fundamental and computational approach to Big Data Science;
  • for the discipline mathematical modelling: preference will be given to applicants with research experience showing the following aspects:

o the research has a distinct fundamental character and is not restricted to the use of mathematical models

o research results are published in mathematics and/or computer science journals

o the research shows a clear link with computer science or with applications in other sciences

  • experience with scientific grant applications;
  • experience in international mobility is recommended; e.g. as demonstrated through prolonged research visits to institutes other than the university where the highest degree was obtained;
  • experience in supervising research and/or coaching Ph.D. students is recommended;
  • teaching qualities to help university students in achieving their full academic potential;
  • a proven track record of successful teaching at an academic level is an asset;
  • continuing professional development in teaching is an asset.

A full-time position at the entry level of assistant professor leads in principle to a five-year temporary appointment within the tenure track system. If the university board arrives at a positive evaluation of the performance of the assistant professor, the position leads to a permanent position as an associate professor.

As an exception to the rule, the candidate may be appointed to a permanent position, either immediately or in due course, based upon similar academic performance at another university or research institution as specified in article 91 of the university decree.

Candidates are required to enroll for the basic training for Assistant Professor.

Ghent University provides free Dutch and English language courses to professors who are non-native speakers in order to support them in their teaching activities.

More detailed information on this vacancy and on the way this job fits in the department’s strategy can be obtained from prof. Willy Govaerts, (tel. +32-9-264-48-93 or ).

At Ghent University, the career plan for professorial staff in the rank of assistant professor and associate professor is based on the periodic evaluation of predefined personalized objectives.

Procedure for application

Applications should be submitted no later than May 25, 2016 at 23h59 (CET) by e-mail to recruitmentzap@ugent.be, together with a letter of application and the university application form for professorial staff (see below). The required transcripts (copies of degrees) also need to be attached (Please be so kind to merge all the documents into one file and to send your e-mail with in the subject line 20160415 and the study field artificial intelligence/big data science/mathematical modelling).

The candidate will receive an e-mail confirming receipt of the application.

The university application form for professorial staff is available via the following link: