PhD - Gent | More than two weeks ago
The rapid advancements in radar technology have led to radar innovations in various applications, including advanced driver assistance, autonomous vehicles, activity recognition, vital sign monitoring and environmental monitoring. Novel machine learning techniques are often used to train radar solutions based on large radar datasets containing annotated time series from pulse radars, mmwave radars of UWB radars. However, the black-box nature of traditional machine learning algorithms often hinders the interpretability of radar-based decision-making systems. This PhD research aims to bridge the gap between radar applications and human understanding by investigating Explainable Artificial Intelligence (XAI) techniques tailored for radar data.
This research will contribute to the field of Radio Frequency (RF) radar applications and artificial intelligence by providing tools and methodologies to enhance the interpretability of radar-based systems.
The PhD dissertation will offer a comprehensive framework for integrating explainable AI techniques into radar applications, improving decision-making, reducing false alarms, and enhancing human-machine collaboration in radar-intensive environments. By shedding light on the decision-making process of radar systems, this research will pave the way for safer and more reliable applications of radar technology in critical domains.
The successful PhD candidate will be part of a large IMEC team working on the design and implementation of AI/ML for wireless communications and networks. This is a unique opportunity to develop innovative, multi-disciplinary technology and share future wireless networks. You will publish your research in top-tier journals and conferences.
Required background: Engineering Technology, Engineering Science, Computer Science, Informatics or equivalent
Type of work: 70% design/modeling/simulation, 20% experimental and 10% literature
Supervisor: Eli De Poorter
Co-supervisor: Steven Latre
Daily advisor: Adnan Shahid
The reference code for this position is 2024-151. Mention this reference code on your application form.