Deakin-Coventry Cotutelle - Knowledge-guided machine learning for optimization problems

This is a doctoral Cotutelle project in 'knowledge-guided machine learning for optimization problems' between Deakin University (Australia) and Coventry University (United Kingdom).' The project is led by Deakin University

Deakin Project Supervisor

Additional Supervision


Deakin Burwood Campus (Australia) and Coventry University (United Kingdom)

Research topic

This is a doctoral cotutelle project between Deakin University (Australia) and Coventry University (United Kingdom).

The successful PhD Student will be awarded a scholarship from Deakin University with the supervision team being drawn from Deakin University and Coventry University. The PhD Student will graduate with two testamurs, one from Deakin University and one from Coventry University, each of which recognizes that the program was carried out as part of a jointly supervised doctoral program. The program is for a duration of 4 years and scheduled to commence in January 2024.

The PhD Student is anticipated to up to 12 months of the total period of the program at Coventry University, with the remainder of the program based at Deakin University.

Knowledge-guided Machine Learning is a growing trend in Artificial Intelligence, where physical models of a phenomenon are used to draw examples to be used in conjunction with already available data. This project will explore the development of Machine Learning models to develop solutions to optimization algorithms that have notoriously high complexity, such as Travelling Sales Person Problem, Convex Hull, Delaunay Triangulation. The project will explore Pointer Networks and their variants, as well as cutting edge transformer-based models and study their application to development of machine learning models to solve complex optimization problems.

Project aim

The project will be built on cutting edge research such as Transformers and Pointer Networks, along with knowledge representation based models such as Bayesian Networks, knowledge graphs, first-order logic, to develop:

  1. Efficient algorithms for solving complex optimization problems,
  2. Demonstrate the effectiveness of knowledge-guidance in solving optimization problems.
  3. Studying the efficacy and power of deep learning-based models for optimization related problems.

Important dates

Applications close 5pm, Wednesday 25 October 2023


This scholarship is supported by Deakin University, is available over 3 years and includes:

  • Stipend of $33,500 per annum tax exempt (2023 rate)
  • Relocation allowance of $500-1500 (for single to family) for students moving from interstate
  • International students only:  Single Overseas Student Health Cover policy for the duration of the student visa
  • Full tuition fee waiver for up to 4 years
  • Funding to support travel of PhD Student between Deakin University and Coventry University.

Eligibility criteria

To be eligible you must:

  • be either a domestic or international candidate. Domestic includes candidates with Australian Citizenship, Australian Permanent Residency or New Zealand Citizenship.
  • meet the PhD entry requirements of both Deakin University and Coventry University, including English language proficiency requirements
  • be enrolling full time
  • be able to physically locate to both Coventry University (UK) and Deakin University (Australia)

Please refer to the research degree entry pathways page and Coventry’s research entry criteria page for further information.

How to apply

Applicants should firstly contact  Dr Nayyar Zaidi to discuss the project. After discussing your application with the Deakin Supervisor, you will be required to submit an Expression of interest directly with the relevant Faculty.
If successful, you will be invited by Deakin University to lodge a formal HDR application.


The successful applicant will also be required to lodge a separate PhD application to Coventry University via the Coventry University application page.

Please be aware that screening for this advert will commence immediately and the scholarship may be awarded prior to the closing date.

Contact us

For more information about this scholarship, please contact:

Dr Nayyar Zaidi
+613 5227 1376
Visit Nayyar's website

Dr Matthew England
Visit Matthew's profile