PhD Scholarship Opportunity: Scalable Artificial Intelligence Decoding for Quantum Error Correction Full-time

PhD Scholarship Opportunity: Scalable Artificial Intelligence Decoding for Quantum Error Correction

Quantum computers suffer from noise or errors which significantly reduce their fidelity to perform large-scale useful computations. To overcome errors, quantum error correction protocols are implemented which detect and correct errors using classical processing known as Decoding. However, decoding must be completed at fast speed and large-scale to ensure errors are fixed before they accumulate. In this project, the student will develop scalable artificial intelligence based methods such as deep neural networks or transformers to perform decoding in microsecond time scales while processing error information from millions of qubits. The project will lead to a timely and critical development for rapidly advancing quantum computing industry and play an important role in enabling real-world applications of quantum computers.

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