Research Fellow (Computational Surrogate Optimization in Python; Industrial Engineering)
The successful candidates will work with Prof. Shoemaker and her group to develop, implement and/or evaluate serial and parallel optimization algorithms for expensive black-box models.
The optimization problem can be expected to have multiple local minima/maxima. Surrogate methods are considered also since computational efficiency for computationally expensive objectives (e.g. simulations) is greatly enhanced with surrogate algorithms and has been coupled with machine learning to solve complex problems. Part of such algorithms are restart strategies.
The candidate will have the opportunity to develop research skills, participate in international conferences, and work on the Singapore Supercomputer (NSCC).
- PhD in Operations Research, Industrial/Systems Engineering, Applied Mathematics, Computer Science or related fields.
- Extensive research experience in computational optimization and good to excellent capability in python programming.
- Prior knowledge of Computational Surrogate Optimization is an advantage. Knowledge of alternative restart strategies is an advantage.
At NUS, the health and safety of our staff and students are one of our utmost priorities, and COVID-vaccination supports our commitment to ensure the safety of our community and to make NUS as safe and welcoming as possible. Many of our roles require a significant amount of physical interactions with students/staff/public members. Even for job roles that may be performed remotely, there will be instances where on-campus presence is required.
Taking into consideration the health and well-being of our staff and students and to better protect everyone in the campus, applicants are strongly encouraged to have themselves fully COVID-19 vaccinated to secure successful employment with NUS.
Location: Kent Ridge Campus
Organization: College of Design and Engineering
Department : Industrial Systems Engineering and Management
Employee Referral Eligible: No
Job requisition ID : 17796