PhD Student

Last application date
Oct 15, 2021 00:00
TW06 - Department of Electronics and Information Systems
Limited duration
Master’s degree in Computer Science, Engineering, Informatics, ICT or related field
Occupancy rate
Vacancy type
Research staff

Job description

PhD position on Knowledge Graphs on the Web

Job description
Semantic Knowledge Graphs are thé way to create interoperable data, which in turn leads to more advanced applications and analytics. However, generating knowledge graphs from existing data is not an easy feat. Existing data is very heterogeneous: differences in volume (small and big), velocity (static and streaming) and variety (different data formats such as JSON, CSV, and XML) result in complex combinations of hard-coded tools, leading to unmaintainable black-box environments. This is why knowledge graph applications are currently reserved for big tech such as Google and Facebook.

The RML Tool Suite provides a one-stop-shop to generate knowledge graphs for any specific domain model from all these heterogeneous data sources using a white-box configurable framework. We lead this research domain with over 8 years of experience and are involved in the standardization process, however, our work is far from done: many research topics are yet to be fully explored, including virtualization of knowledge graphs, automatization of knowledge graph generation, knowledge graph workflows, investigation of performant joining algorithms, etc.

Are you passionate about data, the Web, and interested in working on decentralized knowledge graph technologies? Join our team to work on the next phase of the Web! Under the supervision of a.o. dr. Ben De Meester, expert in high-quality Knowledge Graph generation, you can contribute to bringing knowledge graphs to the masses by tackling knowledge graph generation’s most pressing challenges.

We especially encourage applications by candidates from diverse groups; all are welcome in our team.

Read more about our work on Knowledge Construction here:

Job profile

Your profile
● Degree: Master’s degree in Computer Science, Engineering, Informatics, ICT or related field
● Advanced programming skills in at least 1 major programming language
● Passionate about Web technology
● Fluent in English, spoken and written
● Self-directed and able to perform independent work
● Enthusiastic about working in a research environment
The Knowledge on Web-Scale team at Ghent University – imec
The Knowledge on Web-Scale team ( at Ghent University – imec is renowned for its creative research on Knowledge Graphs on the Web. We apply these techniques on open data, shared data, and personal data in a wide range of application domains such as mobility, digital heritage, scholarly communication, sensor data, governmental base registries, building data, e-health, and logistics.

Our team is part of IDLab ( within Ghent University (, a top-100 university worldwide, located in the heart of Belgium. IDLab is a core research group of imec (, a world-leading research and innovation hub in digital technologies and nanoelectronics. IDLab employs over 300 researchers working on fundamental and applied research on data science and internet technology.

Our offer
You receive the opportunity to perform full-time research in a highly international and friendly working environment, with a competitive salary. You will contribute to existing, and create new open source software libraries. Grounded in fundamental academic research, as a developer you will also support collaborative research with industrial and academic partners in Flanders and on a wider geographic scale in new and ongoing projects.

How to apply

Send your application by email or any questions concerning this vacancy to and indicating “Job Application: PhD position on Knowledge Graphs on the Web” in the subject.

Applications should include
(1) a professional resume,
(2) a personal motivation letter, and
(3) links to open source software contributions

After screening, selected candidates will be invited for an online interview as a first contact in a multi-stage selection process.