Privacy Aware Secure Quantum Machine Learning Platform.
This project aims to improve the security and privacy of quantum artificial intelligence systems by developing a safe and privacy-focused framework for quantum federated learning. It expects to create new knowledge by combining ideas from quantum computing and secure distributed learning. The expected outcomes include better models for quantum threats, new methods to protect private data, and systems that explain how quantum decisions are made. This work helps Australia safely use quantum technology in areas like health and finance, reduce energy use, and support growth in the emerging quantum security sector.
PhD positions
Job description
The successful candidates will work on emerging research challenges in protecting private data, modelling quantum-enabled cyber threats, securing distributed quantum learning systems, and improving the explainability of quantum-enhanced AI decisions. The project has strong application potential in privacy-critical sectors such as healthcare, finance, public services, and critical infrastructure.
Key Responsibilities
The PhD candidates will undertake research related to one or more of the following areas:
- Quantum federated learning and secure distributed quantum machine learning
- Privacy-preserving methods for quantum AI systems
- Security and threat modelling for quantum-enhanced machine learning platforms
- Techniques for protecting sensitive data in quantum and hybrid quantum-classical learning systems
- Explainable quantum machine learning and trustworthy quantum AI decision-making
- Efficient and sustainable quantum machine learning methods
- Development of prototypes, algorithms, simulation environments, and open research tools
- Preparation of high-quality research publications and project reports
- Collaboration with academic researchers and industry partners
Selection Criteria
Applicants should have:
- A strong academic background in computer science, cybersecurity, artificial intelligence, machine learning, quantum computing, mathematics, data science, or a related field
- Interest or experience in quantum computing, machine learning, privacy-preserving AI, federated learning, cybersecurity, or secure distributed systems
- Strong programming skills, preferably in Python and relevant machine learning or quantum computing frameworks
- Good analytical, communication, and academic writing skills
- Ability to work independently and collaboratively in a research team
Desirable Experience
Experience in any of the following areas will be highly regarded:
- Quantum computing or quantum machine learning
- Federated learning or distributed machine learning
- Privacy-preserving machine learning, secure AI, or data protection
- Cybersecurity, threat modelling, or adversarial machine learning
- Explainable AI or trustworthy AI
- Quantum software frameworks such as Qiskit, PennyLane, Cirq, PaddleQuantum, or similar
Postdoc position
Job description
The Postdoctoral Research Fellow will play a leading role in developing novel methods for protecting private data, modelling quantum-enabled cyber threats, designing secure quantum learning systems, explaining quantum AI decisions, and translating research outcomes into practical tools with project partners.
Key Responsibilities
The successful applicant will be expected to:
- Conduct high-quality research on secure and privacy-aware quantum machine learning
- Develop quantum federated learning methods for distributed and sensitive data environments
- Design security and privacy mechanisms for quantum and hybrid quantum-classical AI systems
- Investigate threat models for quantum-enabled attacks and privacy risks
- Develop explainability and trustworthiness methods for quantum AI decision-making
- Build algorithms, prototypes, simulations, and open research tools
- Publish research outcomes in leading conferences and journals
- Contribute to project reporting, milestones, and stakeholder engagement
- Support collaboration with industry partners and help translate research outcomes into real-world solutions
- Mentor PhD students working on the project
Selection Criteria
Applicants should have:
- A PhD in computer science, quantum computing, cybersecurity, artificial intelligence, machine learning, mathematics, data science, or a closely related discipline
- Demonstrated research experience in one or more of the following areas: quantum computing, quantum machine learning, federated learning, privacy-preserving machine learning, cybersecurity, secure distributed systems, or trustworthy AI
- Strong publication record appropriate to career stage
- Strong programming, analytical, and experimental research skills
- Ability to work independently and collaboratively in a multidisciplinary research environment
- Excellent written and verbal communication skills
Desirable Experience
The following experience will be highly regarded:
- Quantum machine learning or quantum AI
- Quantum software frameworks such as Qiskit, PennyLane, Cirq, TensorFlow Quantum, or similar
- Federated learning, distributed learning, or secure collaborative AI
- Differential privacy, cryptography, secure aggregation, or privacy-preserving AI
- Cybersecurity threat modelling or adversarial machine learning
- Explainable AI, trustworthy AI, or responsible AI
- Experience working with industry partners or mentoring research students