Job Description

The offered position focuses on the design of waveforms and channel codes for uncoordinated wireless communications, with applications to future terrestrial (6G) and non-terrestrial (satellite, UAVs) networks. An emergent class of network traffic arises from connected devices (such as autonomous cars, environmental sensors, healthcare monitoring and delivery, smart watches, connected robots) with sporadic communication needs. The development on an unprecedented scale of such traffic —characterized by small packets, sent at unpredictable times, but with strong reliability requirements — pushes the limits of current designs since the mechanisms and protocols classically used to mitigate transmission collision between simultaneously active transmitters are not efficient in the regime of many users and small payloads. The successful candidate will contribute to the design of waveforms and forward-error correction codes for wireless systems with massive-scale over-the-air contention between uncoordinated radio devices.

The goal is to create efficient approaches (in terms of spectrum and energy use, as well as decoding computational complexity) while minimizing the protocol overhead. The design methodology will start with the design of a new theoretical framework relying on probability theory, tensor algebra, and information theory. In this framework, new joint code and modulation techniques will be introduced, and robust multi-user decoding algorithms leveraging message-passing will be developed.

The role will involve devising the mathematical representation of the involved waveforms, deriving analytical performance metrics, algorithmic development, as well as writing up scientific articles and presenting the results in academic conferences.

Required skills:
- a strong background (MSc level) in applied mathematics (probabilities, statistical signal processing, information theory), digital communications, or equivalent
- a taste for theoretical results
- fluent spoken and written technical English
- familiarity with scientific programming (Matlab or Python)

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