IMDEA Networks Institute is a networking research institute whose multinational team is engaged in cutting-edge fundamental science. As a growing, English-speaking institute located in Madrid, Spain, IMDEA Networks offers a unique opportunity for pioneering scientists to develop their ideas. IMDEA Networks is establishing itself internationally at the forefront in the “development of future network technologies“ and has already incorporated highly-reputed scientists (see our research team here). Our researchers share the potential to shape the future of networking science over the coming years.
Further information regarding our areas of research can be found here.
Post-doctoral Researchers at IMDEA Networks Institute are early-stage, post-doctorate researchers who are looking to establish their research career, working with top senior researchers and a team of young, pre-doctorate researchers.
Post-Doc: [Post-Doc Researcher]: [Machine learning and algorithmic aspects in a Human-Centric Data Economy] .
The modern digital economy relies on data as its main resource. With the resurgence of deep neural networks, at the beginning of the decade, a vast array of applications have fuelled economic growth as well as new business models. This project will focus on contemporary problems of machine learning such as model interpretation and feature importance, among others. The interested applicant will work as part of a team with a diverse range of skills, on setting the foundation of a new human-centric data economy, where users become active participants in the value flow, and are remunerated for their efforts.
- N. Laoutaris, “Why Online Services Should Pay You for Your Data? The Arguments for a Human-Centric Data Economy,” IEEE Internet Computing, Vol. 23, No. 5, Dec. 2019. [pdf]
- Goodfellow, Bengio and Courville - Deep Learning book (http://www.deeplearningbook.org/)
- Z. Wu et al - A Comprehensive Survey on Graph Neural Networks (https://arxiv.org/pdf/1901.00596.pdf)
- C. Molnar - Interpretable Machine Learning (https://christophm.github.io/interpretable-ml-book/)
- M. Paraschiv, N. Laoutaris, “Valuating User Data in a Human-Centric Data Economy,” [arXiv:1909.01137].
- PhD in Computer Science, Physics, Mathematics or related technical field.
- Strong background on Machine Learning and Algorithms.
- Strong grasp of Python and relevant libraries for statistics / data science.
- Familiarity with either Tensorflow or Pytorch.
- A background in statistics or at least an understanding of basic notions is strongly desired.
- A desire to work on foundational topics and publish at top conferences.
How To Apply
TO APPLY PLEASE FILL IN THIS ONLINE FORM (https://careers.networks.imdea.org/), choosing the position: Post-Doc: [Post-Doc Researcher]: [Machine learning and algorithmic aspects in a Human-Centric Data Economy] .