Paid PhD internship with Telstra (Melbourne)

Published in the Research School of Physics Event Horizon
Vol49 Issue45 18–22 November 2024

Research to be conducted: 

The proposed WaveSense research aims to advance human presence detection using next-gen mmWave sensing technology combined with AI-driven signal processing. The project will focus on developing innovative methods to accurately detect human presence in real–time, with potential application in smart environments, security, asset monitoring, and more.
The primary objective is to leverage non-invasive capabilities of mmWave signals to create robust sensing systems that can operate in diverse environment, including indoor and outdoor settings. The research will explore how AI models can be applied to mmWave data to improve detection accuracy, adapt to different environmental conditions, and reduce false positives.
The research will involve the design and implementation of advanced signal processing algorithms that can extract meaningful features from the raw mmWave data. These algorithms will be critical in detecting motion and identifying objects. Machine learning techniques will be employed to continuously improve detection capabilities through training on diverse datasets.
Additionally, the project will explore the integration of these sensing systems into practical edge devices. A key focus will be on designing lightweight, energy-efficient algorithms that balance performance with resource constraints, ensuring that the system can run on edge devices without sacrificing accuracy. Techniques such as model pruning, quantisation, and edge-friendly architectures will be explored to make AI models scalable and deployable on small, low-power IoT devices.
The project will also focus on designing a real-world, market-ready product. This project will prioritise professional product design principles, including reliability, scalability, and user experience, ensuring that the solution is ready for industrial use, offering tangible benefits in real-world applications.
 

Skills wish list:  

If you’re a postgraduate research student and meet some or all the below we want to hear from you. We strongly encourage women, indigenous and disadvantaged candidates to apply:

Signal Processing: Strong understanding of advanced signal processing techniques, such as filtering, feature extraction, signal analysis for range and velocity estimation, and clutter removal techniques.
AI and Machine Learning: Strong knowledge of machine learning algorithms, especially in model optimisation, and edge computing frameworks, such as TensorFlow Lite or custom edge solutions.
Electromagnetic Wave Propagation: Understanding of electromagnetic wave propagation, particularly for mmWave frequencies.
Embedded Systems: Experience in the design, development, and integration of embedded systems, such as hardware interfacing, embedded C programming, RTOS. MQTT.
Tools and Techniques: Proficient in Python and embedded C, knowledge of FreeRTOS. Git, debugging tools, and Agile methodology
 

Applications are reviewed regularly and this internship may be filled prior to the advertised closing date of 15th January 2025 if a suitable applicant is identified. Early submissions are encouraged.

For the full advert and how to apply, please head over to the APRintern website.