By 2021, cloud IP traffic will be the most part of an Internet traffic that complexifies with an increasing devices diversity and traffic dynamicity. A proposal framed at the cloud to face this situation is the Knowledge Defined Networking (KDN), where Machine Learning (ML) and Artificial Intelligence (AI) are combined with SDN/NFV and network monitoring to collect data, transform them into knowledge (e.g. models) via ML, and take decisions with this knowledge. Under this paradigm, we aim to design a unified AI-based framework able to learn new efficient cloud network control algorithms. This framework will integrate seamlessly data-driven control (based on ML tools) and model-driven control (based on optimization models), addressing scalability and optimality issues of the cloud control. To do that, we intend to apply two promising AI tools: Deep Learning (DL); and, Reinforcement Learning (RL). You can find more information on this thesis subject on the ARTIC project site: http://www.i3s.unice.fr/~raparicio/project/artic/.
Regulatory diploma required
⁃ Master's degree, engineer or equivalent (ISCED level 7 according to UNESCO) in computer science / mathematics / telecommunications
⁃ Machine learning and data science (namely artificial neural networks)
⁃ Classical optimization theory (convex optimization, combinatorial optimization)
⁃ Computer network control plane (algorithms and protocols)
This thesis is part of the ANR ARTIC project (ARTificial Intelligence-based Cloud network control, cf. http://www.i3s.unice.fr/~raparicio/project/artic/), of which Ramon APARICIO PARDO is the principal investigator. This project will provide the candidate with the funds and resources necessary for their activities (participation in scientific events, equipment, computer, access to computing platforms, etc.)
The thesis will take place in the I3S laboratory (http://www.i3s.unice.fr/), a joint public research laboratory resulting from the collaboration of the CNRS, Univ. Cote d´Azur and INRIA. The I3S laboratory is one of the most important research laboratories in information and communication sciences in the French Riviera and was one of the first to settle in the science and technology park of Sophia Antipolis. It brings together just under 300 people.
The student will work with experts in optimization, machine learning and telecommunications networks from the I3S and INRIA.
The recruitment procedure is likely to be impacted by government measures linked to the Covid-19 pandemic.
Deadline to apply for: June 15th, 2020
To obtain more information, send an e-mail to raparicio en i3s.unice.fr
Supporting documents for the application
1. Curriculum vitae
2. Cover letter
3. Transcript of records (marks) up to the Master degree (essential)
4. Final internship or final year project report (if available)
5. At least, two recommendation letters and a list of the three references to contact