SFI2 open calls for grants/fellowshipS

Post-doctoral fellowship in energy-efficient network slicing with AI/ML

We are seeking candidates for a post-doctoral fellowship position for 18 months to work at the University of São Paulo (USP), São Paulo, and at Computer Networks Group (NUPERC), Salvador University (UNIFACS), Salvador, Brazil. This scholarship is part of Project SFI2 - Slicing Future Internet Infrastructures, which aims to develop an architecture and new mechanisms for slicing resources and services of multiple experimentation network infrastructures.


This post-doctoral fellowship will focus on researching an energy-efficient network slicing approach that efficiently manages energy and saves energy-consuming resources using artificial intelligence/machine learning techniques. The work involves formal system modeling and problem formulation considering energy efficiency requirements and policies when applied in the slicing life cycle process. The work will also include an experimental setup on a distributed testbed infrastructure provided by the RNP.


The candidate is expected to have strong motivation, a solid background in networking, a good experience in software-defined networking (SDN), at least basic knowledge of artificial intelligence/machine learning and optimization, proficiency in programming languages such as C/C++, Java, and Python, sound research and communication skills, including good scientific writing and English speaking, and teamwork capability.


Interested candidates should fill out the online application form: https://docs.google.com/forms/d/e/1FAIpQLSdX1jtypXnNIcVd6GAXGku-ADbQcMAKVghg7xhp_BoYPNB2qw/viewform?usp=pp_url
until JULY 30th, 2022.


Eligibility Criteria: Ph.D. in Computer Science or related areas.

The fellowship includes a monthly stipend of R$ 7.373,10 (about USD 1,350) plus research contingency funds (10% of the annual value of the fellowship each year). For more details, check out FAPESP’s webpage.


Contact:

Profs. Tereza Carvalho or Joberto Martins

terezacarvalho@usp.br or joberto.martins@unifacs.br

Post-doctoral fellowship in AI/ML for resource management/orchestration of 5G slices

We are seeking candidates for a post-doctoral fellowship position for 12 months to work at the Laboratory for Modeling, Analysis and Development of Networks and Computing Systems (LAND) at Systems Engineering and Computer Science Program (PESC), Federal University of Rio de Janeiro (UFRJ), RJ, Brazil.

This scholarship is part of the Project, entitled (SFI2 - Slicing Future Internet Infrastructures), which aims to develop an architecture and new mechanisms for the slicing of resources and services of multiple infrastructures for experimentation.

This work will focus on the use of artificial intelligence/machine learning approaches (AI/ML) for resource management/orchestration of 5G slices in order to accommodate different requirements from diverse services. Formal system modeling and problem formulation are also tasks to be performed in this work. In general, the work will consist in designing and developing AI/ML solutions for resource management/orchestration of virtualized 5G networks. The work will also involve an experimental setup on a distributed testbed infrastructure provided by the RNP.

The candidate is expected to have strong motivation, solid background in mobile networks, at least basic knowledge on artificial intelligence/machine learning and optimization, proficiency in programming languages such as C/C++, Java, and Python, good research and communication skills, including good scientific writing and English speaking, and teamwork capability.

Interested candidates should fill the online application form:

https://docs.google.com/forms/d/e/1FAIpQLSePl5paRQdoGVbNo7AsD_DH7CWuJvPTNjIZa5K_gXM9nL81Dg/viewform?usp=sf_link

until February 25, 2022.

Eligibility Criteria: Ph.D. in Computer Science or related areas.

The fellowship includes a monthly stipend of R$ 7.373,10 (about USD 1,350) plus research contingency funds (10% of the annual value of the fellowship, each year). For more details, check out FAPESP’s webpage.

Contact:

Prof. Rezende

rezende@land.ufrj.br