The ideal applicant will have an outstanding background in Engineering, Computer Science, or Applied Mathematics (or equivalent), as well as experience with the implementation of numerical methods and engineering applications of optimisation techniques (continuous and discrete) in real-time control of dynamical systems with exposure to mathematical foundations of learning, graph theory, system verification, and temporal logic.
 

Applicant Profile:

  • PhD in Engineering, Computer Science, or Applied Mathematics, or equivalent;
  • quality research as evidenced by publications in leading journals and at conferences of systems and control and/or planning and optimisation;
  • expertise in system modelling and control and/or planning algorithms; strong interest in the application of these to address practical problems; in real-time decision-making scenarios;
  • commitment to pursue fundamental research on problems pertaining to real-time decision making in dynamic systems;
  • your initiative; need for minimal supervision; and ability to prioritise tasks to meet timelines;
  • capacity to communicate research concepts to technical and non-technical audiences;
  • ability to work as part of a team that includes graduate and undergraduate students.

See the following link for more information and submitting your application:
http://jobs.unimelb.edu.au/caw/en/job/901101/research-fellow-in-planning-optimisation-and-control