Research Fellow, Computational Optimization in Python
2 days left
- Full Time
- The successful candidates will work with Prof. Shoemaker and her group to develop, implement and/or evaluate serial and parallel single or multi objective optimization algorithms for black-box objective functions.
- 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 them.
- PhD in an optimization/statistical/computational field (e.g. Operations Research, Industrial/Systems
- Extensive experience in developing complex computer codes in Python.
- Prior knowledge of surrogate global optimization 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: 15236