Applications are invited for two PhD scholarships in the areas of (1) real-time decision making for autonomous systems in uncertain environments, (2) secure networked control systems.
The work will be based within the Control and Signal Processing (CSP) Lab, MIDAS Lab, the Department of Electrical and Electronic Engineering, the University of Melbourne. The student will be supervised by Dr Iman Shames (see https://imanshames.blog for more information) and co-supervised by another MIDAS Lab faculty member.
The ideal candidate should have a degree in electrical and electronic engineering, computer science, applied mathematics, or mechanical engineering with solid background in applied mathematics and particularly in numerical methods, control theory, and/or optimisation.
The PhD scholarship provides 30K AUD a year tax-free stipend for up-to four years (subject to passing the PhD candidature confirmation after 12 months) as well as tuition fees for the duration of studies. Additionally, up to 15K AUD will be made available for travel funding to visit other research institutes and attending international conferences.
MIDAS is a large and complementary group of academic researchers and postgraduates collaborating across multiple departments within the Melbourne School of Engineering in areas including automation, control, analytics, machine learning, and optimisation.
Expressions of interest are invited from candidates with (or who expect to gain) a first-class honours degree or an equivalent degree in engineering, computer science, physics, mathematics, or a related discipline. Interested candidates should contact Dr. Shames (ishames@unimelb.edu.au) directly and include (1) a brief statement of interest in this position with clear indication of preference for the 2 areas mentioned above (maximum 2 pages), (2) their detailed CV, (3) bachelors and masters/honours transcripts, and (4) names and contact details of at least two referees.
Application Closing Date: 1 December 2017.
Start Date: As soon as possible from March 2018.
