Machine learning and algorithmic aspects in a Human-Centric Data Economy .
Deadline for receipt of applications: 24 March 2020, 14:00h Europe/Madrid Time
Researcher positions on 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 applicants 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 degree 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.
The selected Researcher will work with the team of Prof. Nikolaos Laoutaris.