Enhancing Aviation Wildlife Management Under Climate Change Crisis Based on Data analytics and machine learning techniques

Integrating data analytics and machine learning techniques into aviation wildlife management presents a transformative opportunity for enhancing safety and sustainability. As the climate crisis alters habitats and migration patterns, the risk of wildlife strikes in aviation also shifts. The project aims to utilize advanced machine techniques to:

1. Assess the effectiveness of the current aviation wildlife management programs in Australia.
2. Identifying Geo-spatial bird hotspots in the airspace.
3. Develop a data-driven interactive analytical aviation wildlife monitoring and risk management system for airports.

The changing climate is reshaping wildlife behavior, presenting new challenges for aviation safety. This project seeks to bring data analytics and machine learning to the forefront of this issue, explicitly targeting the Australian aviation sector. The goals are clear-cut: assess how well current wildlife management strategies are working, identify areas where birds are most likely to intersect with aircraft and develop a sophisticated system to monitor and manage these risks. By doing so, the project aims to transform the aviation industry's approach from a traditionally reactive stance to a preemptive one, thereby enhancing wildlife and air travel safety.

Funding Notes

Griffith University funds both domestic and international PhD candidates on a competitive basis and is one of few institutions to offer both a tuition fee waiver and a living stipend.

To be eligible and competitive for a Griffith University Postgraduate Research Scholarship (domestic; View Website) or a Griffith University International Postgraduate Research Scholarship (international applicants; View Website) you need to have First Class Honours or equivalent research experience.

First-author peer-reviewed publications in international journals are advantageous.

Top-ranked candidates will be selected from among applicants to proceed to a formal PhD and scholarship application through Griffith University, with the support of the prospective supervisory team.

Applications can be received and processed year-round for our four intakes. Click here for key dates.

The PhD candidate, preferably with (i) degree(s) in B.S. or B.Eng. (Hons.) and/or Master in Ecology/Conservation/Computer Science, (ii) an interest in Machine Learning and (iii) at least 2 first-author publications in international peer-reviewed journals, will have the opportunity to collaborate with airport wildlife managers, aviation wildlife management specialists, and IT programmers to gain a comprehensive work experience and also an opportunity for future employment with the project’s industry partners.

The successful PhD candidate is expected to fulfil all of Griffith University’s PhD selection criteria. It is expected that the PhD candidate be based in-person on Griffith’s Nathan campus in Brisbane, Queensland, Australia, even though some travels to Griffith’s Gold Coast campus is also expected.

Location

It is expected that the PhD candidate be based in-person on Griffith’s Nathan campus in Brisbane, Queensland, Australia, even though some travel to Griffith’s Gold Coast campus may be required.