PhD Scholarship Opportunity: Quantum Compiler for Efficient Circuit Transpilation
Job No.: 699202
Location: Clayton campus Melbourne
Employment Type: Full-time
Duration: The scholarship may be held for up to 3.5 years (fulltime) for Research Doctorate (PhD) studies.
Remuneration: The successful applicant will receive
- A scholarship package totalling approximately $41,100 AUD per annum (2026 rate with annual indexation)
- FIT Candidature Funding of $4,000 AUD for the duration of the candidature
- Up to $1,265 AUD from Monash Graduate Research Office as a one-off travel grant
The Opportunity
This PhD project will develop efficient quantum compiler methods to bridge the gap between quantum algorithms and their execution on quantum processors. The project will develop innovative quantum software components and reinforcement machine learning models to enable efficient transpilation of quantum circuits on a wide range of quantum processor modalities and topologies. The key aim of the project is to minimise resource requirements (number of qubits, gates, etc) for quantum circuit execution.
The Scalable Quantum Systems Lab, directed by Professor Muhammad Usman, is home to a range of innovative tools and projects – including developing applications of quantum computing Brisbane 2032 Olympics Games, National Quantum Computing Testbed Facility, and Quantum Artificial Intelligence for Defence. The successful PhD candidate will be part of a world-class research program interacting with post-doctoral researchers and PhD students at the Faculty of Information technology.
To be considered for this opportunity you should fulfil the eligibility requirements listed below.
- Both Australian citizen or permanent residents and international students will be considered for this opportunity.
- A bachelor’s degree of at least four years in a relevant discipline, which includes a research thesis or project, with a minimum overall average grade of an honours degree equivalent to the First Class Honours; or
- A master's degree in a relevant discipline which includes a research thesis or project equivalent to at least 25 percent of one year of full-time study, with a minimum overall average grade of honours equivalent to the First Class Honours; or
- A qualification, or combination of qualifications and relevant professional experience, deemed equivalent by the GRC (or delegate).
- Meet university English language requirements..
- Not have previously completed a PhD.
- Be able to commence the Program in the year of the offer.
The ideal PhD candidate will have:
- A strong background in machine learning, deep learning, and software programming
- Proficiency in Python and machine learning frameworks such as PyTorch (required)
- Knowledge of quantum computing or quantum algorithms (advantageous but not essential)
- Strong analytical skills, curiosity for interdisciplinary research, and the ability to collaborate effectively with both academic and industry teams
Monash University strongly advocates diversity, equality, fairness and openness. We fully support the gender equity principles of the Athena SWAN Charter.
This position has a two-stage selection process:
Stage 1: Please submit your interest to Professor Muhammad Usman at muhammad.usman@monash.edu
With the EOI please include the documents - CV, full academic transcripts that shows your GPA or weighted average mark as well as the grading scale, a cover letter and a draft research proposal of up to 2 pages, responding to one or more of the above research objectives.
The draft research proposal should highlight your prior research experience and interest in the quantum computing field.
Stage 2: Candidates who pass this stage of the selection process will be invited to discuss their ideas with the supervisory team before developing and submitting a full application.
Enquiries: fit-graduate.research@monash.edu or muhammad.usman@monash.edu
Please submit your application as soon as possible for this opportunity to join Monash University. Interviews will be held as strong applicants are identified. Applications will close when the role has been filled, no earlier than Tuesday 15 December 2026, 11:55pm AEDT
Supporting a diverse workforce
