PHD STUDENT AND POSTDOC POSITIONS ON 6G V2X COMMUNICATIONS

The UWICORE laboratory at the Universidad Miguel Hernández (UMH) de Elche (Spain) offers several post-doc and PhD student positions on 6G Vehicle-to-Everything (V2X) Communications.
Research will focus on solutions for designing scalable 6G V2X networks for Connected, Cooperative and Automated Mobility (CAM), with a focus on novel semantic V2X communications, AI-based collective awareness and networking, and V2N (vehicle to network)-based solutions. The research will design novel solutions for a paradigm shift in V2X communications where, rather than focusing on ensuring the reliable and timely transmission of data (addressing the technical problem identified by Shannon and Weaver), we focus, like humans do, on selecting and transmitting the relevant information to scalably convey the desired message for the intended receivers and the target applications (addressing the semantic and effectiveness problems identified by Shannon and Weaver). The relevance of the information becomes the focus of the communications process, and such relevance is highly dependent on an accurate knowledge of the context of the communications process. To this aim, the rich communications and sensing V2X ecosystem where vehicles and infrastructure/network collaborate represent an opportunity to develop scalable 6G V2X networks.
Tasks
The candidates will contribute to one or several of the following research activities in the context of 6G V2X:

Design of novel Artificial Intelligence (AI)-based solutions to process contextual information, generate collective awareness (including with the support of the infrastructure), and determine the relevance and semantic value of information.
Design of semantic and goal-oriented V2X communication protocols for scalable 6G V2X networks, including novel semantic Key Performance Indicators (KPIs) and scalable implicit feedback mechanisms.
Design of Vehicle-to-Network-to-Everything (V2N2X) solutions for deterministic support of CAM services in an interoperable multi-stakeholder framework with data sharing in the IoT-edge-cloud continuum.
Development of an advanced CAM simulation platform that integrates a realistic 3D modelling of the driving environment (CARLA) with a realistic autonomous driving software stack (AUTOWARE) and V2X connectivity

The scope of this project is aligned with the Strategic Research and Innovation Agenda of the Horizon Europe CCAM partnership, the EU 6G Smart Networks and Services (SNS) programme, and the UN Sustainable Development Goals (SDG8, SDG11, SDG 13).
Requirements
Post-doc candidates should have a PhD in Computer, Telecoms, Electrical or Robotics Engineering (or closely related fields), and a high-quality track record of publications in relevant journals and conferences. Preferably, the candidate should have done the PhD or have experience in one of the following research topics: Artificial Intelligence (Deep Learning, Reinforcement Learning), V2X communications, autonomous driving, and autonomous robotics. Experience with discrete-event simulators, Robot Operating System (ROS), autonomous driving simulators (CARLA) and software (Autoware), and the design of AI-based solutions will be positively considered. Proficiency in programming languages such as C++ and Python, and experience with software development, debugging, and deployment is required. Experience with machine learning software such as PyTorch, Keras, or TensorFlow is a plus. Strong theoretical foundations and analytical modelling experience are also a plus. Critical thinking skills, team working attitude, and self-motivation are required. Proficiency in English writing and speaking, advanced communication and presentation skills to illustrate complex technical concepts are also required.
PhD candidates should have a Master in Telecommunications, Electrical, Computer, or Robotics Engineering (or closely related disciplines). Interest or experience in one of the following topics is required: 5G and beyond wireless networks, V2X communications, Artificial Intelligence (Deep Learning, Reinforcement Learning), autonomous driving, and autonomous robotics. The candidate should have good programming skills (C++, Python) as well as good theoretical foundations, analytical modelling and critical thinking skills. Publications in journals and conferences are positively considered but not required. Experience with network simulators (ns-3, OMNeT++), Robot Operating System (ROS), or machine learning software (PyTorch, Keras, or TensorFlow) is a plus. Good English writing and speaking skills are required, as well as team working attitude, self‐motivation and a strong desire to utilize technology for improving society.