About this role
Research Associate in Machine Learning
Locations: Wallumattagal Campus, Macquarie University
Time type: Full time
Posted: Yesterday
Job requisition id: R000024571
Advance the theoretical foundations of machine learning, publish in premier venues and help shape the next generation of AI research.
Annual salary package: $104,481 (ACA Level A.4) - $112,550 (ACA Level A.6/ PhD awarded rate) plus 17% superannuation & annual leave loading Full-time, fixed term appointment until end of July 2027 Macquarie University, Wallumattagal Campus, North Ryde
Machine learning is moving beyond centralised data and single models. Deep variational and decentralised learning sit at the frontier of this shift, and the theory that underpins them is still being written. This is an opportunity to help write it.
The Role
We seek a highly motivated and competitive research associate to work on foundational research in deep variational and decentralized learning. This role gives you unique opportunities to
- Explore deep variational and decentralized machine learning theories
- Develop theoretical foundations and publish results in competitive venues
- Work with a competitive team with wide global representativeness
Under the supervision of the project leader, you will contribute to a focused program of foundational AI research. Key responsibilities include, but are not limited to:
- Develop fundamental models for deep variational and decentralized learning.
- Conduct relevant modelling, analytical and visualization tasks.
- Publish the findings in top-tier events.
About Us
The Faculty of Science and Engineering is a community of discovery, dedicated to developing the next generation of science and technology innovators. Within the Faculty, the School of Computing brings together researchers, educators and students working across artificial intelligence, data science, cyber security, software engineering and information systems.
We are a group of highly active, dynamic and productive research team based in the AI Lab in the School of Computing and the Frontier AI Research Centre at Macquarie University. The team is committed to high-profile research activities relating to major grants, industry engagement, and international collaboration. The team consists of hierarchical diverse profiles of stakeholders from senior leaders to active academic staff, HDR students, postdoctoral researchers, to visitors.
About You
You are an experienced researcher with relevant qualification in the areas relating to AI, data science, and machine learning; demonstrated experience in coordinating a research team and assisting research leaders and academic staff; demonstrated outcomes in applied statistics, advanced analytics, machine learning, optimization methods, information theory, visualization, etc. areas through contributing to research projects; skills and satisfactory results in communicating with broad stakeholders including intensive publication experience.
Selection Criteria
- Master by Research or higher level of qualifications in the areas of AI, data science, machine learning, or relevant areas,
- Demonstrated research expertise in areas including applied statistics, machine/deep learning, information theory, visualization, and data analytics,
- Evidence of research outputs such as a strong track record in continuously publishing at top-tier venues as the main contributor, and an
- Ability to collaborate with relevant teams, partners, participants or stakeholders.
Role Specific Requirements
The successful candidate must be able to work within any applicable project confidentiality, contracting and security requirements.
The successful candidate will be required to undergo a National Police/ Criminal History Check and a qualification check. An international police/ criminal history check may also be required, depending on the candidate's circumstances.
Appointment will also be subject to compliance with applicable Defence-related legislation and project security requirements, including Defence Export Controls and Safeguarding Australia’s Military Secrets legislation where relevant.