The University of Melbourne is seeking applications for PhD study at the intersection of machine learning and constrained optimization. Six scholarships are being targeted to support study in this area (valued at $AUD30,000 per annum). Students with backgrounds from a range of disciplinary areas are encouraged to apply, including computer science, data mining, machine learning, constraint programming, operations research and mathematical optimization.
Project area: Current decision making approaches often draw on two key technologies: i) machine learning, and ii) optimization. Machine learning delivers predictive models and forecasts about quantities like demand and yield, given process input. Optimization takes forecasts for demand and yield, as well as other constraints, and delivers optimal policies for action. This project area is exploring how these activities can be more tightly integrated, since they have traditionally been performed independently. The overall aim is to investigate the deep integration of constrained optimization and machine learning, and to provide more effective tools for society to tackle the challenging decision making problems it is facing.
Project team: This project is funded by an Australian Research Council Discovery Grant "Data driven decision making for complex problems", with investigators Prof James Bailey, Prof Christopher Leckie, Prof Rao Kotagiri (University of Melbourne), Prof Peter Stuckey (Monash University), Dr Jeffrey Chan (RMIT University), Prof Ian Davidson (University of California Davis), Dr Tias Guns (Vrijie Universiteit Brussel).
Interested applicants are invited to send an expression of interest, comprising:
Academic supervisors will review EOIs received by the closing date. EOIs will be used to establish an applicant's eligibility and competitiveness for being admitted to a PhD funded by a scholarship. Selected candidates will be further invited to make a formal application for admission to the PhD program at the University of Melbourne, with one or more supervisors from the above project team.