01 July 2019

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For more information please contact

Prof. Dr. Luc De Raedt, mail:;

Prof. Dr. ir. Hendrik Blockeel, mail:;

Prof. Dr. Jesse Davis, mail:; 

You can apply for this job no later than September 30, 2019 via the online application tool
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Apply before 30 September 2019

The successful candidates will be employed on a project that involves fundamental research in AI / machine learning / data science, with a clear application potential.  

The lab for Declarative Languages and Artificial Intelligence (DTAI) of the Department of Computer Science of the KU Leuven is one of the leading research groups for machine learning and data mining. DTAI’s machine learning group counts four faculty members (Luc De Raedt, Hendrik Blockeel, Bettina Berendt and Jesse Davis), one research manager, around 5 post-docs and over 25 doctoral students.

DTAI is internationally renowned for its expertise in integrating different forms of reasoning (inductive, deductive and probabilistic), in logical learning, statistical relational learning, probabilistic programming, learning from structured data (relational databases and graphs), inductive logic programming, inductive databases, action-activity learning, knowledge representation, data mining, and constraint programming.

Next to fundamental research in machine learning and data mining, the DTAI group applies the developed techniques to concrete cases situated in intensive care monitoring, predictive maintenance, smart self-diagnosis, mechatronics, robot manipulation and navigation, natural language processing, smart electronics, computer vision, etc. For these applications, DTAI cooperates with other groups and companies from strategically chosen research areas.

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The successful candidates will be employed on a project that involves fundamental research in AI / machine learning / data science, with a clear application potential.  Research will be conducted on the following topics:1) AI-assisted data and knowledge acquisition and data wrangling: data and knowledge are not always immediately available in the right format.  How can we semi-automatically extract them from technical documents (CAD drawings, tables, reports, ...), or build programs that perform this extraction?
2) automated feature construction: learning and reasoning work much better when done at the right level of abstraction.  This requires introducing the right features.  This is hard to do manually.  How can we automate it?
3) active learning: given the cost of acquiring data, we want to focus on getting the most useful data or knowledge.  How do we estimate the potential usefulness of data and knowledge for a specific task?
4) flexible supervision: learning algorithms can be made more effective by enabling them to exploit user input not only in the form of labels (supervised / semi-supervised learning) but also in the form of constraints, inductive bias, or domain knowledge.  They should also be able to handle missing knowledge of this kind.
5) probabilistic logic programming: a very powerful class of models are those that combine probabilistic inference with first-order-logic reasoning.  These models are very expressive: they can solve very complicated questions.  They can also be learned from data, but the state of the art in this area leaves room for improvement, for instance regarding scalability.  We wish to investigate more scalable parameter and rule learning systems.

The research on these topics will be steered towards practical applicability. To guarantee this, concrete use cases have been developed in collaboration with companies.  The research will partly be evaluated by means of these use cases.


The ideal candidate has a master degree in Computer Science or Artificial Intelligence.  Experience that relates to the topics of the projects or the expertise of the Machine Learning group is a plus. 

Candidates must have excellent programming skills, be proficient in oral and written English, possess excellent communication skills, multi-tasking skills, and be team-oriented, proactive and result driven. The PhD students will work on research projects that involve frequent interactions with both internal and external researchers.

The positions can start immediately, and positions will be filled as soon as suitable candidates are found. Interested PhD candidates should send their CV, their transcripts, contact information of 2 or 3 referees, and a motivation letter using the KU Leuven system. The motivation letter should clearly identify the topic(s) the candidate wants to work on, and it should clearly specify the reasons for these choices, as well as possible past experience in the area.


A full time PhD scholarship of 1 year, extendible until max. 4 years.

A stimulating environment at Europe's most innovative university, in a well-equipped, experienced and internationally oriented research unit. 

The research will be based at the Department of Computer Science at the Arenberg Campus in Heverlee (close to the center of Leuven).