Research Fellow in Machine Learning for Automated Decision Making

Location
Melbourne, Australia
Posted
20 Apr 2021
End of advertisement period
04 May 2021
Ref
0052425
Contract Type
Fixed Term
Hours
Full Time

Work type: Fixed Term
Location: Parkville
Division/Faculty: Faculty of Engineering and Information Technology
Department/School: School of Computing and Information Systems
Salary: $73,669 - $99,964 (Level A)
Role & Superannuation rate: Academic - Full time - 17% super

The University of Melbourne is consistently ranked among the leading universities in the world, we are globally engaged; comprehensive; research-intensive; and committed to responding to the major challenges of our time.

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:

The Research Fellow in Machine Learning for Automated Decision Making will contribute to Australian Research Council Centre of Excellence for Automated Decision-Making and Society (ADM+S) on Fairness in Route Recommendation Systems, which is a three-year research project. This project will 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 transportation applications but can potentially be applicable in other areas. The position will be part of a team comprising investigators from the University of Melbourne and RMIT University. The successful applicant 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.

The Fellow will be based at the School of Computing and Information Systems in the Faculty of Engineering and Information Technology at the University of Melbourne, and affiliated with the ARC Centre of Excellence for Automated Decision-Making and Society. You will collaborate actively with the Centre of Excellence’s national and international network of participants. You will conduct independent and collaborative research, leading to the preparation and publication of research outcomes in conferences and journals. All Centre postdoctoral research fellows will also have access to the Centre of Excellence’s research training and professional development opportunities, international visiting fellowships and exchanges (travel arrangements permitting), and the Centre’s network of industry partners. You will be expected to be an active member of the School of Computing and Information Systems and the Centre of Excellence, collaborating with other researchers. You may have the opportunity to undertake small amounts of teaching, conduct training activities and research supervision directly related to your area of research.

About the Centre of Excellence

The rapid expansion of automated decision-making enabled by technologies from machine learning to the blockchain has great potential benefits, while it also creates serious new risks to human rights and welfare. Potential harms range from data discrimination against disadvantaged communities to the spread of disinformation for political and commercial ends. Increasing inequality, lower productivity and diminished economic security have been highlighted as risks in the coming decade.

The ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S) is a new, cross-disciplinary, national research centre, which aims to aims to create the knowledge and strategies necessary for responsible, ethical, and inclusive automated decision-making. Funded by the Australian Research Council from 2020 to 2026, ADM+S is hosted at RMIT in Melbourne, Australia, with nodes in seven other Australian universities including the University of Melbourne. The Centre brings together leading researchers in the humanities, social and technological sciences in an international industry, research and civil society network. Its priority domains for public engagement are news and media, transport, social services and health.

About You:

Naturally you will be capable of demonstrating your prior record of achievement in the following:

  • PhD in computer science or a relevant discipline
  • High-quality algorithmic research in machine learning as evidenced by research publications in leading conferences and journals, commensurate with opportunity
  • Ability to perform independent research in machine learning for optimisation and recommendation systems;
  • Capacity to communicate research concepts to technical and non-technical audiences
  • Excellent ability in problem solving and critical thinking
  • Excellent written and verbal communication skills, demonstrated by presentation of research results at conferences, internal forums and through manuscript submissions;
  • Excellent interpersonal skills, including an ability to interact with internal and external stakeholders (academic, administrative and support staff) in a courteous and effective manner
  • Demonstrated experience in using initiative, working with minimal supervision and ability to prioritise tasks to achieve project objectives within timelines

Additionally, it would be desirable (but not necessary) if you if you also had experience in relation to any of the following:

  • Demonstrated expertise and research track record on developing novel algorithms for topics such as: machine learning for transport routing and recommendation systems; explainability, trust and fairness of machine learning
  • Experience in training, testing and analysing the complexity and performance of different types of machine learning algorithms on diverse and voluminous data sets
  • Experience in supervision of graduate students and/or research assistants
  • Experience in working with external partners in government or industry to define new research or training projects
  • Experience in delivering training activities to research students or external partners

You will be supported to pursue achievement in all four pillars of an academic career:

  • Research
  • Teaching and Learning
  • Engagement
  • Service and Leadership

What we offer you

We offer flexibility, whatever that may mean for you. Many of our benefit programs and onsite amenities are aimed at supporting you - including generous leave, childcare subsidies, discounted parking, medical and health care. We offer extensive opportunities for personal and professional development, and we’ll support you in doing what you love.

We seek to increase the diversity of our workforce and the representation of all members of our community that have been traditionally under-represented.

If you’re curious, motivated and ready to undertake a meaningful and rewarding role we’re ready to meet you.

How to Apply

Apply online, complete the application and upload your Cover Letter - addressing all selection criteria; and your Resume.

PLEASE NOTE: You must be currently located within Australia and able to commence immediately or as soon as your notice period has been served.

You will be an Australian Citizen; Permanent Resident or holder of an appropriate visa.

While we review your application, get to know us by visiting http://www.eng.unimelb.edu.au/about/join-mse/why-join-mse

Position Description

Download File 0052425.pdf

Applications close: 04 May 2021 11:55 PM AUS Eastern Standard Time

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