NATIONAL UNIVERSITY OF SINGAPORE

Senior Research Fellow, Computational Global Optimization in Python

Location
Singapore
Posted
Friday, 19 November 2021
End of advertisement period
Sunday, 19 December 2021
Ref
5028144
Contract Type
Permanent
Hours
Full Time

Description and Location:

Applications are requested for Senior Research Fellow for computational global optimization and/or Python Software Development with Prof. Christine Shoemaker, Distinguished Professor in the Department of Industrial and Systems Engineering at the National University of Singapore. The position focuses on computational surrogate optimization algorithms.

Research Focus and Goals

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. The candidate will have the opportunity to develop research skills, participate in international conferences, and work on the Singapore Supercomputer (NSCC).

Review of applications will begin immediately and continue until the position is filled.

Job applications and inquiries should be sent to Prof. Shoemaker at shoemaker@nus.edu.sg. Applicants should include a vita and indicate desired start time in the email message. Please also put “Job Application-SRF  2021” in the subject line of the email being sent.  Prof. Shoemaker will contact applicants if more information than what they have submitted would be helpful.

Job Requirements

  • A PhD Degree in Operations Research, Industrial/Systems Engineering, Applied Mathematics, Computer Science or from similar programs.
  • Min. 3 years post PhD experience.
  • Extensive experience in developing complex computer codes in Python.
  • Prior knowledge of surrogate global optimization is an advantage.

More Information

Location: Kent Ridge Campus
Organization: Engineering
Department : Industrial Systems Engineering And Management
Employee Referral Eligible: No
Job requisition ID : 7701

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