Research Fellow in Machine Learning for Automated Decision Making

Job no: 0052425
Location: Parkville
Role type: Full time; Fixed-term for 2 years
Faculty: Faculty of Engineering and Information Systems
Department/School: School of Computing and Information Systems
Salary: Level A – $77,171 – $104,717 p.a. plus 17% super

• Grow your career contributing to a collaborative research project in Fairness in Machine Learning and Optimisation
• Work with The University of Melbourne, RMIT University and the ARC Centre of Excellence on ADMS
• Bring your experience working in developing novel algorithmic methods for machine learning, especially for recommendation systems and optimisation problems

The University of Melbourne would like to acknowledge and pay respect to the Traditional Owners of the lands upon which our campuses are situated, the Wurundjeri and Boon Wurrung Peoples, the Yorta Yorta Nation, the Dja Dja Wurrung People. We acknowledge that the land on which we meet and learn was the place of age-old ceremonies, of celebration, initiation and renewal, and that the local Aboriginal Peoples have had and continue to have a unique role in the life of these lands.

The Faculty of Engineering and Information Technology (FEIT) is strongly committed to supporting diversity and flexibility in the workplace. Improving the representation of women is necessary in our goal to innovate and to strengthen FEIT’s reputation as a best-in-class centre of research.

About the School of Computing and Information Systems (CIS) We are international research leaders with a focus on delivering impact and making a real difference in three key areas: data and knowledge, platforms and systems, and people and organisations.

At the School of Computing and Information Systems, you'll find curious people, big problems, and plenty of chances to create a real difference in the world.

About the Role
Fantastic opportunity to play a key role in our collaborative research project between The University of Melbourne, RMIT University and the ARC Centre of Excellence for Automated Decision-Making and Society. The Research Fellow in Machine Learning for Automated Decision Making will contribute to the Fairness in Machine Learning and Optimisation research project which aims to develop new approaches that combine fairness, privacy and legal guarantees for automated decision-making systems, such as recommender and machine learning based systems.  It will initially focus on applications in transport and disaster response, but can potentially be applicable in other areas. 

To ensure the University continues to provide a safe environment for everyone, this position requires the incumbent to hold a current and valid Working with Children Check.

Responsibilities include:

• Conducting high-quality research on machine learning for recommendation systems and other topics relevant to the Machines program in the CoE, both independently and as part of project teams
• Participating in preparation of manuscripts for publication in peer-reviewed conferences and journals
• Contribute to teaching, training, scientific mentoring and supervision of students, including co-supervision of research projects for students at Masters and PhD level

About You
You possess strong interpersonal and communication skills with demonstrated ability to work cooperatively in a multi-disciplinary team environment and engage with a diverse range of stakeholders. Your problem solving, critical thinking and organisational skills ensure the effective management and completion of project objectives within agreed deadlines.

You will have a background in computer science (or related discipline) with a strong research track record in developing novel algorithmic methods for machine learning, especially for recommendation systems and optimisation problems. Experience in research into the fairness, trust and explainability of machine learning models would also be an advantage.

You will also have:

• PhD in computer science or a relevant discipline
• A track record of high-quality algorithmic research in machine learning as evidenced by research publications in leading conferences and journals, commensurate with opportunity
• Demonstrated ability to perform independent research in machine learning for optimisation and recommendation systems
• Capacity to communicate research concepts to technical and non-technical audiences

For specific responsibilities of this role please refer to the attached Position Description.

About the University
The University of Melbourne is consistently ranked amongst the leading universities in the world. We are proud of our people, our commitment to research and teaching excellence, and our global engagement.

Benefits of Working with Us
In addition to having the opportunity to grow and be challenged, and to be part of a vibrant campus life, our people enjoy a range of rewarding benefits:

• Flexible working arrangements, generous personal, parental and cultural leave
• Competitive remuneration, 17% super, salary packaging and leave loading
• Free and subsidised health and wellbeing services, and access to fitness and cultural clubs
• Discounts on a wide range of products and services including Myki cards and Qantas Club
• Career development opportunities and 25% off graduate courses for staff and their immediate families

To find out more, visit https://about.unimelb.edu.au/careers/staff-benefits .

Be Yourself
We value the unique backgrounds, experiences and contributions that each person brings to our community and encourage and celebrate diversity.  First Nations people, those identifying as LGBTQIA+, females, people of all ages, with disabilities and culturally and linguistically diverse people are encouraged to apply. Our aim is to create a workforce that reflects the community in which we live.

Join Us!
If you feel this role is right for you, please apply with your CV and cover letter outlining your interest and experience.  Please note that you are not required to provide responses against the selection criteria in the Position Description.

We are dedicated to ensuring barrier free and inclusive practices to recruit the most talented candidates. If you require any reasonable adjustments with the recruitment process, please contact us at [email protected] .

Applications close: Thursday 1 September 2022 11:55 PM AUS Eastern Standard Time

Position Description