PhD Research Fellow in Artificial Intelligence and Machine Learning tools for UAV communication

The successful applicant will conduct research in the project Ubiquitous Connectivity via Autonomous Airborne Networks (AirBonnet). This project comprises a set of key developments intended to develop the technology of unmanned aerial vehicles (UAVs) such as zeppelins, balloons, and multicopters that autonomously navigate through the airspace to provide data connectivity in those locations where it is absent or unsatisfactory.

While households in developed countries receive skyrocketing data rates through optical fibers and smartphones step into the 5G era, roughly one half of the world’s population cannot connect to the internet. Even beyond developing economies,  bringing data connectivity to areas where it cannot currently reach would drastically benefit applications such as the internet-of-things, smart agriculture/forestry, wildfire suppression, search-and-rescue missions, paramedical interventions, and emergency response handling to name a few.

To this end, the UAVs in the targeted technology are equipped with a communication module that connects to the ground users on one side and to the cellular terrestrial infrastructure on the other side. The user information may even be relayed through multiple UAVs before reaching its destination. For this technology to be viable, the UAVs must be able to navigate without human supervision to locations with favorable propagation conditions, that is, where the signals that they receive from and transmit to the ground users and cellular infrastructure are not significantly blocked by obstacles such as buildings or mountains.

The key approach in this project is to construct "radio maps" that describe the propagation conditions in a certain region. Using these maps, the UAVs rely on artificial intelligence algorithms to determine the appropriate locations and can even adapt to changes in the user positions and connectivity requirements as well as to coordinate with other UAVs. Our preliminary findings already showcase the ability of artificial deep neural networks to construct radio maps from a small number of measurements collected by the UAVs.

This position involves theoretical research, computer simulation, measurement collection, and testbed development.

A prerequisite for employment is that the candidate is to be admitted to UiA’s PhD programme at the Faculty of Engineering and Science, Specialisation in Engineering Sciences. The goal of the PhD program is to train students to become autonomous researchers. This means that students need to complete a coursework component of 30 ECTS intended to provide the necessary background for conducting research. Additional self-study will also be required to fill possible background gaps or specialized knowledge requirements.

A close advising model is generally pursued at the beginning, whereas more independence is gradually granted to the student as she/he becomes more mature. Ideally, the student must be able to conduct research independently after graduation.

Part of the objectives of any PhD program is to develop the student’s communication skills, both oral and written. The PhD program intensively and advising style will promote and assist in the development of these skills.

The research topic’s links to the research environment at the Faculty

Top Research Centre Mechatronics: The project is in line with the activities of the newly established Top Research Center in Mechatronics (TRCM), affiliated with the Department of Engineering Sciences. In particular, important synergies will be created with the following Research Themes (RT): Human-Robot Interaction and Collaborative Robots.

The research focus of this position is on control theory, aerial robotics, and artificial intelligence. Candidates whose profile is more focused on wireless communications, machine learning, and signal processing should apply to this position.

Required qualifications