PhD Neural Principles of Multi-Sensor Vision Systems in Rail Safety Applications

The project will develop new deep learning methods to improve machine vision comprising several sensors in collaboration with industry partners 4AI Systems and 4Tel Pty Ltd.

In this project we will investigate principles of multi-sensor vision systems in the context of deep neural networks and sensor fusion. The aim is to utilise machine learning techniques to develop new real-world technology in machine vision. The project will be conducted at the Newcastle Robotics Lab in Australia in collaboration with industry partners 4AI Systems and 4Tel Pty Ltd who specialise on rail transport safety.

The PhD candidate will be part of an interdisciplinary team of researchers from computer science and industry. The candidate should have good programming skills, knowledge in machine learning, an interest in relevant mathematical concepts and sound engineering judgement. The research focus can include, but is not limited to deep networks, graph neural networks, generative models, topological data analysis and stereo vision.

PhD Scholarship details

Funding: $32,192 per annum (2024 rate) indexed annually. For a PhD candidate, the living allowance scholarship is for 3.5 years and the tuition fee scholarship is for four years. For an MPhil candidate, the living allowance and tuition fee scholarships are two years. Scholarships also include up to $1,500 relocation allowance and Overseas Student Health Cover at single rate, for an international candidate.
The scholarship will be offered to the successful candidate subject to the grant/funding being fully established.

Supervisor: Professor Stephan Chalup

Available to: Domestic and International students

Either PhD or MPhil

Eligibility Criteria

The applicant will need to meet the minimum eligibility criteria  for admission.

Application Procedure

Interested applicants should send an email expressing their interest along with scanned copies of their academic transcripts, CV, a brief statement of their research interests and a proposal that specifically links them to the research project.

Please send the email expressing interest to [email protected] by 5pm on 01 June 2024.