imec is the world-leading research and innovation hub in nanoelectronics and digital technologies. The combination of our widely acclaimed leadership in microchip technology and profound software and ICT expertise is what makes us unique. By leveraging our world-class infrastructure and local and global ecosystem of partners across a multitude of industries, we create groundbreaking innovation in application domains such as healthcare, smart cities and mobility, logistics and manufacturing, and energy.
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 machine learning for global change ecology.
The research project
With an earth, which is undergoing a severe and global change in terms of climate, we are being faced with important new challenges to understand and mitigate the negative effects. The IDLab research group is working together with researchers from the Global Change Ecology Excellence Centre at the University of Antwerp (
In this multi-disciplinary research project, you will be focused on applying and improving upon new machine learning algorithms for studies in global change ecology. Besides applying your work in a multi-disciplinary setting, there will be need for designing new machine learning algorithms that are specifically tuned to the nature of the research (e.g., able to cope with large unsupervised datasets, the ability to do real-time inference on remote sites, the ability to cope with heterogeneous and noisy data, etc.).
For more information, please contact