6G-TERRAIN, a newly launched Marie Skłodowska-Curie Innovative Doctoral Network (DN) aims to propose, design, and validate a novel Native AI automation architecture and its associated enabling technologies and theoretical frameworks, aiming for an intent 6G System with seamlessly integrated multi-stakeholder & multi-technology (e.g., terrestrial and non-terrestrial) nodes. Natural Language intents are adopted, exploiting the breakthrough capabilities of emerging LLMs to automatically plan the optimal decompositions of intents into tasks and priorities their downstream execution. Finally, 6G-TERRAIN will build a training network to conduct top-notch research towards the development and experimental evaluation of a gamut of techniques, methodological frameworks and tools that will break the barriers currently impeding the realization of the 6G “Network of Networks” vision. 

More information on the project can be found here: https://www.6g-terrain.eu/#home 

Iquadrat Informatica is searching for two (2) interested candidates in fully funded Doctoral Candidates positions by the European Commission DN program 6G-TERRAIN, under the supervision of Dr. Kostas Ramantas and Dr. John Vardakas.

These prestigious European Grants for PhD studies are designed to train highly skilled doctoral candidates, stimulate their creativity, enhance their innovation capacities and boost their employability in the long-term. The program offers very competitive salaries, making it an excellent opportunity for those looking to advance their careers.

Open positions 

1. Native AI decision automation approaches in telecommunication networks

   The objective of this position is to develop Native AI decision automation approaches in telecommunication networks that support real-time, conflict-free adaptation of system intents. The validation of conflict resolution schemes within the IQU B5G testbed, targeting measurement-based and real-world experimentation results and datasets related to conflict resolution.

2. Intent translation and actuation

   The objectives of this position is to investigate existing open-source LLM-based systems for intent translation through few-shot learning or fine-tuning, and mainly to devise an LLM for translating human language into Infrastructure Level Intent (ILI), and deliver intent actuation via self-managed control loops.