THMMY.gr

Μετά τη Σχολή => Σπουδές μετά τις Σπουδές => Topic started by: Cthulu on January 11, 2018, 00:21:49 am



Title: Δϋο θέσεις υποψηφιων διδακτόρων στην Eurecom της Γαλλίας
Post by: Cthulu on January 11, 2018, 00:21:49 am
Επισυνάπτονται οι προκυρήξεις για δύο θέσεις διδακτορικού φοιτητή στο ερευνητικό κέντρο Eurecom στο τεχνολογικό πάρκο Sophia-Antipolis που βρίσκεται κοντά στην Νίκαια της Γαλλίας. Παρακάτω παραθέτω τις περιγραφές των δύο θέσεων, πληροφορίες μπορείτε να βρείτε και στο site της Eurecom. Για όσους ενδιάφερονται, θα έλεγα τσιμπήστε το, είναι καλή περίπτωση ;)

1. PhD Position: Cooperative Caching and Transmission in 5G Networks
Description: The goal of this thesis will be to jointly optimize cooperative caching and cooperative transmission in future 5G networks. A key goal will be to explore the tradeoff between edge caching that reduces backhaul traffic, and caching that improves radio access performance. At the center of this tradeoff lies the question of how caching algorithms can adapt to accommodate potential Coordinated Multi-Point (CoMP) transmission opportunities. A second key goal is to investigate distributed implementations of the proposed optimal solutions, in order to (a) deal with the high complexity of cooperative caching problems, and (b) significantly reduce the amount of additional (signaling) information transmitted over the already congested backhaul links.  

2. PhD Position: Joint Optimization of Content Recommendation and Caching for MEC-enabled Future Wireless Networks
Description:This position is funded by an ANR “Jeunes Chercheurs” (Young Investigator) grant at the  Comm. Systems department at EURECOM, Sophia-Antipolis (http://www.eurecom.fr/en). The goal of this thesis will be to bring together the theories of Cooperative Caching and Recommendation Systems. The main two novel aspects to exploit, currently ignored in most systems, will be to make sure that: (i) Caching algorithms take advantage of how users select the contents they consume, which is increasingly driven by sophisticated recommendation systems (e.g. Netflix, YouTube, Spotify, etc.) as well as social network influences (Facebook, Twitter, etc.); (ii) Recommendation algorithms of key applications become aware of how the recommended content gets delivered to the user, and “bias” their recommendation to improve performance for both the user and the network operator. The selected student will be expected to develop an expertise on: (i)  state-of-the-art caching and recommendation algorithms, (ii) modern optimization theory and machine learning aspects, and (iii) 5G and beyond system evolutions like cloud-computing and MEC (mobile-edge computing).