- Full-time, 2 years FTE
- Geelong Waurn Ponds Campus
- Level B $99,346 + 17% Superannuation
A2I2 pioneer methods and approaches for AI and Machine Learning problems with high potential impact. Our interests cover Machine Learning, Reinforcement Learning, Deep Learning, Optimisation and Software Engineering.
Based at the Applied Artificial Intelligence Institute (A2I2) at Waurn Ponds location (formerly PRaDA), the Research Fellow will be responsible for contributing towards the research objectives within the field of strategic research areas of interest to A2I2. It is important that the Research Fellow contributes to the profile and research reputation of A2I2, by means including publications, public lectures, seminars, contributing to public debates and policy formation on key research issues.
Your key responsibilities will include:
- Initiating and conducting research under limited supervision either as a member of a team, or independently (where appropriate), to achieve the objectives of the relevant project or research agenda as specified by the Director.
- Contributing to successful development of research programs or partnerships.
- Conducting research and engaging in scholarly publications (e.g. ICML, NeurIPS, AAAI, ICLR, CVPR etc), personally and in research teams and prepare findings/results for oral and written communication, producing or contributing to the production of conference and seminar papers and publications from that research.
- Preparing and developing grant applications relating to the project(s), and contribute to the preparation, or where appropriate, individual preparation of research proposal submissions to external funding bodies.
- Co-supervise, or where appropriate, supervise, honours or postgraduate research students, or making a contribution to the supervision, management to ensure successful and timely completions of HDR students with the quality of results.
To be successful, you’ll have:
- PhD in Computer Science or equivalent qualification
- Research experience in the area of Machine Learning, which has resulted in top quality publications, conference papers, reports, or professional or technical contributions, which give evidence of research ability.
- A research track record in Machine Learning with a demonstrated ability to plan, initiate and conduct high quality research.
- Ability to develop collaborative work teams and to work effectively as a member of the team.
- Direct or indirect experience in contributing to the supervision of undergraduate honours and / or research higher degree students.
- Strong foundational knowledge in mathematical optimisation, linear algebra and statistics.
For a copy of the position description, please see below:
Position Description - Research Fellow- 504795.pdf
Applications for this position close on 13 November 2020.
This role requires the incumbent to apply for and maintain a Working With Children Check (refer to Deakin’s Recruitment Procedure for further details).
Please submit your updated resume and a short cover letter outlining your skills and experience for this role.
For a confidential discussion regarding this position, please contact Sunil Gupta at firstname.lastname@example.org
Are You Ready?
Deakin is a Victorian university with a global impact. We are an agile, dynamic and innovative university committed to making a positive impact through our excellence in education, research and innovation and the contributions we make to the wider community.
We understand that our reputation has been built on the dedication and expertise of our staff and we offer a dynamic and diverse working environment with opportunities to grow and develop careers. We believe that a progressive, thriving culture will ensure that people choose to come, and stay at Deakin and contribute to our ongoing success.
We value diversity and aim to build an inclusive environment that champions, embraces and respects differences. We support and encourage applications from Aboriginal and Torres Strait Islander people, and people of all cultures, sexual orientation, and genders.
We understand that our academic workforce is increasingly diverse, and we recognise academic careers may be placed on hold throughout many life circumstances. Achievement relative to opportunity places more emphasis on the quality, as opposed to the quantity of research outputs. In your application, we strongly encourage you to comment on your achievements relative to opportunity.