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Noise reduction and machine learning approaches for advanced electromagnetic spectrum sensors

The University of New South Wales (UNSW Sydney) School of Systems and Computing
✓ Fully Funded ⏰ Closing Soon 🎓 Computer Science 🎓 Electrical Engineering 🎓 Machine Learning 🎓 Signal Processing machine learning Defence Trailblazer RF circuits SDR UAV sensors antenna design electromagnetic spectrum industry-linked noise reduction

This industry-linked PhD focuses on miniaturized EMS sensor hardware, leveraging machine learning and signal processing to reduce noise, optimize ultra-small form-factor antennas, and improve manufacturability. The project includes potential industry internships and Defence Trailblazer collaboration.

Project Description

The project addresses physical limitations in miniaturizing electromagnetic spectrum (EMS) sensors, aiming to: Apply machine learning and digital signal processing to minimize induction noise in software-defined radios (SDRs) for compact applications. Explore novel methods to reduce the form factor of ultra-wideband conformal and planar array antennas. Investigate techniques to improve manufacturability of ultra-small SDRs, antennas, and RF circuits. Advances in these areas could enhance nano-scale EMS sensors for UAVs, static low-profile installations, and defence or commercial applications. Alternate related topics may also be considered in consultation with the supervisor.

Entry Requirements

Preference for candidates from Five Eyes countries (Australia, New Zealand, USA, Canada, UK) or domestic PhD/Research Master’s candidates.
Strong background in electrical engineering, computer science, or related areas.
Interest in applied research with industry impact.

How to Apply

Submit a completed expression of interest form and academic transcripts to defence.trailblazer@unsw.edu.au
. For queries, contact the same email.

Further details: HDR Application Process

Eligibility

UK/Home
EU
International

Supervisor Profile

AP
Assoc Prof Benjamin Turnbull
The University of New South Wales (UNSW Sydney), School of Systems and Computing

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