Quantum Machine Learning Scientist

Apply now Job no:522559
Work type:full time
Location:Sydney, NSW
Categories:Post Doctoral Research Associate, Senior Research Associate

• Join a world-class team at the cutting edge of technology, focussed on making quantum computers a reality
• Collaborate with a diverse, multicultural team across the full stack quantum computer
• Enjoy the sunny shores and stunning scenery of Sydney, Australia

The Quantum Machine Learning Scientist works within a truly multi-disciplinary team of scientists and engineers at the forefront of atomic electronics and quantum computing internationally, funded by Silicon Quantum Computing Pty Limited (SQC) and based at UNSW Sydney.

This role will work closely with hardware, software and processing engineers to realise quantum processors in silicon ensuring high-fidelity control and measurement of multi-qubit devices at cryogenic temperatures. The Quantum Machine Learning Scientist will develop different machine learning protocols, including reservoir computing, extreme learning machines and kernel methods, for near-term implementations of both analogue and digital quantum computers. They will use SQC’s analogue quantum devices, comprised of large arrays of phosphorus-doped silicon quantum dots, as a computational resource to solve certain well-defined computational tasks to demonstrate potential advantage over classical computational methods. Additionally, this role will develop machine learning models for digital quantum computers focusing on protocols that can be implemented with a 100-qubit silicon-based quantum processor, as the broader SQC team works to scale quantum devices from 10-qubit to 100-qubit architectures within the next 5 years. Where necessary, this role will assist with the support and supervision of PhD and undergraduate students and other members of the Algorithms and Architectures team members.

The Quantum Machine Learning Scientist will report to Dr Casey Myers, the Algorithms and Applications team lead, and also work closely with Associate Professor Charles Hill, the Quantum Architectures team lead, alongside other staff and students funded by SQC.

About the role

• Fixed Term – 3 years
• Full-time (35 hours per week)

Background

• SQC is an Australian research and development company, with the specific aim of building a quantum computer based on atom qubits in silicon.
• SQC is a well-funded Australian company formed by the Commonwealth Government, the New South Wales State Government, Commonwealth Bank of Australia, Telstra, and UNSW Sydney.
• SQC is seeking to commercialise silicon quantum computing technology developed in Australia – technology that has the potential to have a global impact.
• SQC’s work is building on more than 20 years of world-leading research by the Centre of Excellence which includes the development of dedicated manufacturing and measurement techniques for an atom-based quantum computer in silicon.
• This role and SQC are located at the headquarters of the Centre at UNSW Sydney.

Specific responsibilities for this role include: 

LEVEL A: 

• Develop quantum machine learning models with near-term quantum systems.
• Design and implement machine learning protocols with analogue quantum systems using near-term arrays of phosphorus-doped silicon quantum dots.
• Design and implement machine learning protocols with digital quantum algorithms on near-term, atom-based silicon hardware.
• Adapt, tailor, and develop quantum machine learning protocols for industry focussed applications.
• Work on the theory of quantum machine learning to determine when/how a quantum advantage can be achieved for both analogue and digital quantum computing.
• Work with a multidisciplinary team of quantum physicists, engineers, technicians, postdoctoral researchers, and PhD students for early-stage quantum system implementations on silicon-based quantum computer devices.
• Generate high quality research publication output.
• Provide technical assistance and training to the research staff and students working within SQC.
• Align with and actively demonstrate the UNSW Values in Action: Our Behaviours and the UNSW Code of Conduct.
• Cooperate with all health and safety policies and procedures of the university and take all reasonable care to ensure that your actions or omissions do not impact on the health and safety of yourself or others.

 LEVEL B:
(in addition to the above)

• Make significant contribution to the field of quantum machine learning.
• Where appropriate, take leadership of research projects.
• Supervise honours or other higher degree research students

About the successful applicant
(Selection Criteria)

To be successful in this role you will have:

LEVEL A:             

• A PhD in Physics, Computer Science, Mathematics, or relevant field, with work experience in a research or commercial environment.
• Demonstrated experience in classical machine learning techniques, with expertise in reservoir computing, extreme learning machines or kernel methods.
• Demonstrated research capabilities in quantum machine learning or a related field.
• Experience writing high-performance quantum algorithm implementation, preferably in Python, MATLAB, C/C++, or equivalent. GPU programming and/or high-performance computing experience.
• Experience with Python quantum computing packages such as OpenQASM (Qiskit), Cirq, Project Q, or equivalent.
• Experience in troubleshooting and solving complex unplanned issues.
• Well-organised, attention to detail and ability to meet deadlines.
• High level written and verbal communication skills and the ability to network effectively and interact with a diverse range of students and staff.
• Demonstrated ability to work in a team, collaborate across disciplines and build effective relationships.
• An understanding of and commitment to UNSW’s aims, objectives and values in action, together with relevant policies and guidelines.
• Knowledge of health and safety responsibilities and commitment to attending relevant health and safety training.

 LEVEL B:
(in addition to the above) 

• Demonstrated ability to conduct independent research in machine learning or quantum algorithms with a track record in research with outcomes of high quality and high impact with clear evidence of the desire and ability to continually achieve research excellence as well as the capacity for research leadership.
• Demonstrated significant experience in classical machine learning techniques, with expertise in reservoir computing, extreme learning machines or kernel methods.
• Demonstrated outstanding research capabilities in quantum machine learning or a related field.
• Experience solving problems with machine learning on quantum systems.
• Experience modelling qubit systems.
• Significant experience with Python quantum computing packages, such as OpenQASM (Qiskit), Cirq, ProjectQ, or equivalent.
• Experience with co-supervision of higher degree research students in quantum computing.

You should systematically address the selection criteria listed within the position description in your application.

Please apply online – applications will not be accepted if sent to the contact listed.

Contact:
Casey Myers
E: [email protected]

Applications close: February 26th, 2024

Find out more about working at UNSW at www.unsw.edu.au

UNSW aspires to be the exemplar Australian university and employer of choice for people from diverse backgrounds. UNSW aims to ensure equality in recruitment, development, retention and promotion of staff and that no-one is disadvantaged on the basis of their gender, cultural background, disability, sexual orientation or identity or Indigenous heritage. We encourage everyone who meets the selection criteria to apply.

UNSW partners with Australia’s leading diversity organisations, networks, and campaigns. Please refer to UNSW’s diversity offerings for further information on our flexible work and leave options, and support for carers (childcare, parent rooms, parental leave).

Advertised:29 Jan 2024 AUS Eastern Daylight Time
Applications close:26 Feb 2024 AUS Eastern Daylight Time