Join Our Research Team

At Graph Research Lab @ ANU, we are searching for passionate, talented, and motivated researchers eager to push the boundaries of graph theory, machine learning, and network science. Whether you’re an ANU student or a prospective PhD candidate, our dynamic research environment offers you opportunities to engage in groundbreaking projects, collaborate with leading experts, and build a successful career in academia or industry.

Currently, we are offering two PhD positions (one of which must start in 2025, ideally between September and December, due to funding requirements).

Why Join Graph Research Lab @ ANU?

  • Cutting-edge research: Work on high-impact projects that advance the fields of deep learning and routing optimization.
  • Collaborative environment: Join a multidisciplinary team with a strong track record of influential publications and conference presentations.
  • Career growth: Benefit from world-class mentorship, access to state-of-the-art facilities, and a supportive research culture.

1. PhD Opportunity: “Deep Learning for Graphs”

Position Overview:
This PhD opportunity invites you to tackle grand challenges at the intersection of deep learning and graph theory. By leveraging state-of-the-art techniques and recent advances, you will develop novel heuristics, scalable architectures, and theoretical frameworks to unlock the potential of graph-structured data while applying these innovations to solve real-world problems.

What You’ll Do:

  • Explore new research questions in deep learning applied to graph-structured data.
  • Develop and test novel techniques and theoretical models.
  • Collaborate with a multidisciplinary team on high-impact projects.
  • Contribute to publications and conferences in leading forums such as NeurIPS, ICLR, ICML, and AAAI.

Eligibility:

  • A Bachelor’s or Master’s degree in Computing or Mathematics, with First-Class Honours (or equivalent).
  • Demonstrated excellence in programming, with proficiency in languages such as C, C++, or Python.
  • Proven research experience and a strong academic interest in machine learning, deep learning, and/or graph theory.
  • Solid foundation in algorithm design and analysis, with excellent analytical and problem-solving skills.

Application:
Email your application and inquiries to qing.wang@anu.edu.au with the subject “PhD Application”. Include:

  1. A one-page motivation letter.
  2. Your CV.
  3. Academic transcripts.
  4. Samples of prior research (e.g., thesis, publications).

2. PhD Opportunity: “Routing Optimization”

Position Overview:
We invite applications for a PhD position focused on advancing research in routing optimization, graph algorithms, and network science. Hosted at the Graph Research Lab within the School of Computing at ANU, this role will enable you to develop innovative algorithms to optimize routing in complex, dynamic networks—addressing challenges in transportation, logistics, and urban planning.

What You’ll Do:

  • Design and implement innovative algorithms to optimize routing in complex, dynamic networks.
  • Engage in collaborative research that bridges theoretical developments with practical applications.
  • Contribute to high-quality publications and present your work at leading conferences such as SIGMOD and NeurIPS/ICLR/ICML.

Eligibility:

  • A Bachelor’s or Master’s degree in Computing or Mathematics, with First-Class Honours (or equivalent).
  • Demonstrated excellence in programming, with proficiency in languages such as C, C++, or Python.
  • Proven research experience and a strong academic interest in machine learning, deep learning, and/or graph theory.
  • Solid foundation in algorithm design and analysis, with excellent analytical and problem-solving skills.

Application:
Email your application and inquiries to qing.wang@anu.edu.au with the subject “PhD Application”. Include:

  1. A one-page motivation letter.
  2. Your CV.
  3. Academic transcripts.
  4. Samples of prior research (e.g., thesis, publications).

Note: All applicants must apply for admission following the ANU HDR policy and meet ANU’s language requirements.

For more details on related projects and publications, please visit our research page.