Research Fellow in Machine Learning for Engine 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 related to the control and calibration of diesel engines. The team maintains a longstanding partnership with Toyota Motor Company in this area.

This position is available for 2 years and will be reviewed at the end of this period.

The aim of the research is to explore and develop novel algorithms that are implementable in real-time to improve the fuel economy and emissions of diesel engines. Given the constraints present in the system, much of the work to date has focused on the development of model predictive control designs. One of the challenges in implementation of these controllers is the calibration to achieve the appropriate level of performance under different legislated limits and consumer demands for different markets. We intend to develop methods (based on a combination of machine learning and traditional optimisation methods) that can partially automate the calibration process for these advanced controller designs, leading ultimately to faster calibration of better performing engine controllers.

The research fellow must have a background in engineering or applied mathematics, with demonstrated expertise in modelling and control of dynamical systems and numerical optimisation. Experience with at least one of model predictive control; engine modelling and control; machine learning; and implementation of controllers on real systems is essential – while some background in more than one of these areas is highly desirable.

Candidates are encouraged to apply via this link.

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