PhD in Computer Science / Mechanical Engineering
Optimization problems involve maximization/minimization of certain performance objectives such as maximization of strength, maximization of fuel efficiency, minimization of weight, etc. This is done by parametrizing the model of the design and searching systematically for the combination of parameters that will yield the best performance. During the process of optimization, a large number of designs may need to be evaluated to achieve a near-optimal or satisfactory design. However, this becomes impractical when the evaluation of each design is computationally expensive. For example, when the design performance evaluation involves time-consuming simulations such as Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), etc.
This research aims to develop methods that can drastically reduce the number of evaluations required during the optimization to achieve competitive results. The focus will be metaheuristic approaches (such as evolutionary algorithms) coupled with surrogate modelling and management strategies. Various problems involving practical challenges such as presence of multiple conflicting performance objective and design constraints will be considered.
Good programming (Matlab, C/C++) and analytical skills, preferably with a Masters Degree in Engineering / Computer Science. Prior research experience in optimization is desirable but not necessary. Demonstrated competence in academic writing and oral presentation skills will be beneficial.
Must meet UNSW admission criteria and English Language requirements. Scholarships of AUD 35,000 per annum are available for PhD students joining UNSW Canberra who achieved H1 /High Distinction in their UG program and/or have completed a Masters by Research. For more details and eligibility, visit https://www.unsw.adfa.edu.au/study/scholarship/international-postgraduate-research-scholarships.
Please send electronic copies of CV and transcripts to Dr Hemant Kumar Singh (firstname.lastname@example.org).
Multidisciplinary Design Optimization (MDO) Group, UNSW Canberra (http://mdolab.net/)