PhD - Leuven | More than two weeks ago
Explore novel neural encodings at the radar chirp level to reduce the traditional radar processing pipeline and extract features for spiking neural networks early enabling accurate, fast and energy-efficient multi-object identification
The use of mm-wave radar for gesture, gait or pose recognition is a challenging and active field of research. However, future algorithms need to process multiple objects in a scene instead of identifying a single object, which requires segmentation and identification. Moreover, some application, such as drone or robotic autonomy, requires ultra-low latency, low-power implementations. The Ph.D. aims to investigate novel approaches, exploring faster, lower computation complexity and efficient radar signal encoding challenging the traditional radar signal processing pipeline. Moreover, we will use spiking neural networks (SNNs) to replicate closely how the brain process information. SNNs can be implemented on chips requiring ultra-low power and intrinsically processing temporal information, which is ideal for processing radar data.
In summary, the ideal candidate will focus on the following research questions:
Required background: Computer science or Electrical Engineering or Applied mathematics or Physics, or relevant. Proficiency in programming python/c/c++/embedded. Any sort of artificial intelligence or radar theory background is useful.
Type of work: 60% algorithm implementation, 30% Embedded HW programming, 10% benchmarking
Supervisor: Steven Latré
Co-supervisor: Werner Van Leekwijck
Daily advisor: Inton Tsang
The reference code for this position is 2023-097. Mention this reference code on your application form.