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PhD: Constrained and black-box optimisation using population-based algorithms
Where: University of Technology Sydney, Australia Australia
What: Location: University of Technology Sydney – City campus. UTS is #1 young university in Australia in 2019 based on QS and THE ranking. Also, UTS is ranked #29 globally and #1 in Australia for the subject area of Computer Science and Engineering (based on 2019 ARWU rankings)

Project Details: This project is about using population-based and black-box algorithms for solving challenging optimization problems. Several topics are going to be investigated in this project including constrained optimization, multi-objective optimisation, and surrogate models.

Requirements: A solid background in computer science and optimisation as well as strong programming skills (MatLab/Python). A master degree in computer science, data science, mathematics, AI, engineering, or related field. Basic statistics knowledge and familiar with Bayesian Optimization. Background in evolutionary computation topics like multi-objective and constrained optimization. Publications with major conferences and/or journals.

Who: https://www.uts.edu.au/staff/amirhossein.gandomi
When: Until 2020-07-30 10:00
Presented at next GECCO?: yes



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