NATIONAL UNIVERSITY OF SINGAPORE

Research Fellow, Computational Global Optimization in Python

Location
Singapore
Posted
18 Nov 2021
End of advertisement period
18 Dec 2021
Ref
4338944
Contract Type
Permanent
Hours
Full Time

Job Description

Description and Location:

Applications are requested for Research Fellow (Postdoc) 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-RF  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.  Hopefully applicants would be available to start work by August 2021.

Job Requirements

  • A PhD Degree in Operations Research, Industrial/Systems Engineering, Applied Mathematics, Computer Science or from similar programs. Position is also available to those close to receiving PhD.  
  • 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: NUS Environmental Research Institute
Department : Research
Employee Referral Eligible: No
Job requisition ID : 6885

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