Research Fellow in Optimisation and Control

Department of Electrical and Electronic Engineering
School of Electrical, Mechanical and Infrastructure Engineering
University of Melbourne

Salary: PhD entry Level A.6 $87,415 -$93,830 p.a.) plus 9.5% superannuation

The research fellow will join a team of academic staff and postgraduate students working on problems pertaining to real-time decision making in dynamic systems. The team maintains a longstanding partnership with Defence Science and Technology Group in this area.

In this research, the investigation will focus on the coordination of a group of autonomous vehicles to achieve a common objective under uncertain and time varying operational considerations. The flexibility to be able to handle different scenarios and associated mission constraints will be an integral aspect of the research.

The aim of the research is to explore and develop novel algorithms that are implementable in real-time, can predict over a long horizon and make optimal decisions under given performance metrics while satisfying hard constraints. This will involve a combination of predictive control and machine learning (data-driven) techniques to optimally respond to rapidly changing conditions, and to disrupt the planning algorithms of adversarial actions.

The research fellow will have an outstanding background in Engineering or Applied Mathematics (or equivalent), and experience with the implementation of numerical methods and engineering applications of optimisation techniques in real-time control of dynamical systems with exposure to mathematical foundations of learning, graph theory, and combinatorial optimisation.

The research fellow will be located in the Department of Electrical and Electronic Engineering within the Melbourne School of Engineering, and collaborate with researchers and engineers internally and externally. In addition to preparing technical reports, research publications, and computer simulations, the research fellow may also have the opportunity to undertake teaching and student supervision is areas directly related to their research.

Candidates are encouraged to apply via this link.

Application Closing Date: 31 December 2017.
Start Date: As soon as possible from March 2018.