PhD Scholarship in "Machine Learning for Evaluating Constraints in Optimization Algorithms"

PhD Scholarship in "Machine Learning for Evaluating Constraints in Optimization Algorithms"

This project develops state-of-the-art Combinatorial Optimization (CO) algorithms using machine learning techniques and meta-heuristics (e.g., evolutionary algorithms) to learn the values of constraints.

Combinatorial Optimization Problems (COPs) are ubiquitous, and many optimization methods have been developed for tackling COPs. In the big data era, we are facing an increasing need to tackle large-scale COPs. Gaining a solid understanding of the usefulness of constraints and making the best use of their values can play a crucial role in tackling this challenge. 

We are inviting applications for a fully funded PhD position focused on developing state-of-the-art optimization algorithms using machine learning techniques to learn the values of constraints. More specifically, we will investigate how to best characterize constraints in the context of population-based meta-heuristics (e.g., evolutionary algorithms) and use machine learning to learn the values of the constraints for a specific COP, e.g., vehicle routing problem. Our objective is to use this knowledge extracted from machine learning to boost the performance of the optimization algorithm.   

This project will be supported by our ARC Discovery Grant (DP250103251) "Learning to Value Constraints", building on our strong publication records in machine learning for combinatorial optimization for the past few years. The PhD student will be based at RMIT University. However, there will be opportunities to interact and collaborate with other investigators and research fellows in the team from Monash University and La Trobe University.

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A stipend of $35,886 per annum pro rata (full-time study), with a possible 6-month extension

Open now

31/10/2025

1 (one)

To be eligible for this scholarship you must:

  • have a first-class Honours or 2A Honours or equivalent degree in computing technologies;  
  • be an Australian citizen, Australian permanent resident or an international student meeting the minimum English language requirements;  
  • provide evidence of adequate oral and written communication skills; 
  • meet RMIT's entry requirements for the master by research degree (or master coursework including a minor thesis).

Interested candidates should contact Professor Xiaodong Li (xiaodong.li@rmit.edu.au), Professor Andreas Ernst (andreas.ernst@monash.edu), or Dr. Yuan Sun (Yuan.Sun@latrobe.edu.au). Please provide a short research proposal outlining your interest and alignment with the proposed research, and why you think you are the best candidate for this project. You should also provide a short CV, your academic transcripts, 2 of your top published research papers (if any).

Ideal CandidateWe are looking for a highly motivated candidate with a strong background in one or more of the following areas:

  • Machine Learning / Graph Neural Networks
  • Combinatorial Optimization / Meta-heuristics
  • Applied Statistics or Data Science
  • Essential skills:
    • Proficiency in Python; 
    • C++/C or Rust are also highly desirable
  • Strong operations research background
  • Passion for machine learning and optimization
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Acknowledgement of Country

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