Research Fellow (EXPIRED)

Job no: 540003
Work type: Fixed Term
Location: Canberra / ACT
Categories: Academic

Classification: Academic Level B
Salary package: $99,809 - $113,165 plus 17% superannuation
Terms: Full time, Fixed Term, 2 years.

• Opportunity to design novel approaches for decision-making and learning, with an application to develop robust and strategically empathetic robots.
• Opportunity to join a major cross-disciplinary research project, the ANU Humanising Machine Intelligence (HMI).
• Opportunity to leverage a variety of expertise in decision-making under uncertainty, to develop novel approaches in robust decision-making and learning for robots. 

Position overview

The HMI is a major cross-disciplinary project at the ANU, uniting a team of computer scientists, philosophers, and social scientists in the pursuit of a more ethical future of machine intelligence. We share a common expertise in probabilistic decision-making, though coming from different perspectives. Such a multi-disciplinary background provides a holistic view of automated decision-making that the team, including the to be appointed Research Fellow, can leverage.

The Research Fellow position will be based at the ANU’s School of Computing and collaborate closely with team members within and across discipline to make substantial progress towards ethical AI. The school is a community of high performing academic and professional staff, students and visitors sharing a deep commitment to transforming the future of computing for the next generation. It is a leading centre for research in artificial intelligence and machine learning, computer systems and software, and theoretical foundations of computing.

The successful candidate will have completed, or nearly completed a PhD in Computer Science, Artificial Intelligence, Robotics, or  disciplines relevant to the HMI project with experience in one or more of the following fields: Planning under uncertainty, motion planning under, multi-agent planning, robotics, reinforcement learning, robust control, or algorithmic game theory. Experience in applying research results from one of the mentioned areas to a physical robot is a plus.