PhD Researcher on Resource Efficient Deep Learning

Antwerpen - PhD
More than two weeks ago

The IDLab research group of imec  is looking for a PhD researcher to conduct research in the IDLab distributed intelligence team, enhancing, applying or extending existing algorithms and creating new ones.


University of Antwerp – imec IDLab Research group

The IDLab research group of imec and the University of Antwerp performs fundamental and applied research on internet technologies and data science. The overall IDLab research areas are machine learning and data mining; semantic intelligence; distributed intelligence for IoT; cloud and big data infrastructures; multimedia coding and delivery; wireless and fixed networking;  electromagnetics, RF and high-speed circuits and systems. Within Antwerp, IDLab specifically focuses on wireless networking and distributed intelligence.  IDLab has a unique research infrastructure used in numerous national and international collaborations.
IDLab collaborates with many universities and research centres worldwide and jointly develops advanced technologies with industry (R&D centers from international companies, Flanders’ top innovating large companies and SME’s, as well as numerous ambitious startups).
For further development of the IDLab machine learning research cluster, we are looking for a PhD researcher in the domain of resource efficient deep learning 


  • You have or will soon obtain a Master of Science degree, preferably in Computer Science, Engineering, Mathematics, Biology, Physics, Electrical Engineering, or equivalent.
  • You have experience with programming languages and software development
  • You have a profound interest in artificial intelligence and machine learning. Knowledge on deep learning and/or reinforcement learning is an additional asset.
  • You are interested in the application domain of wireless networks
  • You are a team player and have strong communication skills.
  • Your English is fluent, both speaking and writing.

The research project

Deep Learning has provided tremendous breakthroughs over the last few years in domains such as natural language process, computer vision and video game strategies. Part of this success is due to the availability of huge amounts of (labeled) data and ample computing power. As a result, many of the deep learning successes require an architecture where a large neural network model can be trained and inferred in the cloud.
On the contrary, many real-life applications require the complete opposite model. For both technical and non-technical reasons, learning and/or inference should happen at the edge of the network, where resources are much scarcer. This scarcity can either be a lack of (labeled) data, computing power, energy, networking resources or a combination of the above. In this research project, you will be developing new deep learning algorithms that are specifically optimized for such resource poor environments. Examples of these include label efficient learning algorithms, learning in sparse reward settings, effectively employing simulation, learning on spatio temporal data, etc.

The job

  • You are conducting research in the IDLab distributed intelligence team, enhancing, applying or extending existing algorithms and creating new ones.
  • You will implement novel deep learning algorithms, and conduct experiments to compare the new algorithms with existing state-of-the-art methods
  • You will write detailed evaluation reports on the developed algorithms and executed experiments, publishing them in major conferences and journals.
  • You will develop demonstrators in the framework of European and national research projects (in collaboration with industry).


If you are interested in this vacancy or you want more information: please contact


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