Postdoctoral researcher in AI

The DTAI lab of KU Leuven’s Department of Computer Science is an internationally leading research group in machine learning and knowledge-based artificial intelligence. It currently includes 14 faculty members, 4 research managers, 19 post-docs and over 70 doctoral students, geographically spread over 5 locations. This position is at the Arenberg campus in Leuven.
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Responsibilities

The postdoctoral researcher will be embedded in the Flanders AI Research Program (FAIR) and will have as main tasks: conducting research, co-supervising doctoral students, helping with project administration (e.g., reporting).   In addition to this, there are opportunities for involvement in teaching in one of the four academic programs that the DTAI lab is involved in (BSc informatics, Master of Engineering in CS / AI, Advanced master of AI).

The Flanders AI Research Program involves all Flemish universities and multiple organizations that interface between academia and industry.  Its main aim is to facilitate the uptake of cutting-edge AI research results by industrial and societal stakeholders in Flanders.  Researchers in this program conduct fundamental research (as evidenced by academic publications in top-tier conferences and journals) but also devote part of their time to use cases with direct practical relevance for industry, society, etc.

For this particular position, the main research area is machine learning and data science, with a focus on time series analysis, verification and certification of learned models (neural networks, decision tree ensembles), and tensor decompositions. 

Profile

Candidates must have a PhD in Computer Science or a closely related field.  Extensive knowledge and skill in core computer science areas (algorithms, data structures, computational complexity, …) is expected. The ideal candidate furthermore has:

- experience with supervising bachelor/master students

- experience with the following specific topics in machine learning / data science: time series analysis; tensor decompositions; verification of learned models; ensembles of decision trees; neural networks; use of domain knowledge (typically in the form of constraints). 

- experience with both fundamental and applied research (as witnessed by academic publications in top conferences and journals, as well as involvement in more applied projects)

Offer

More information

DTAI research lab
Flanders AI Research

Interested?

For more information please contact Prof. dr. ir. Hendrik Blockeel, mail: [email protected].

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