Research Engineer, Computational Global Optimization in Python
Description and Location:
Applications are requested for Research Engineer 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 firstname.lastname@example.org. Applicants should include a vita and indicate desired start time in the email message. Please also put “Job Application-RE 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.
- The person should have a Bachelor Degree or Masters’ Degree in a topic where mathematical text is used frequently (e.g. CS, math, or Engineering).
- Enrollment in a PhD program in an optimization/statistical/computational field (e.g. Operations Research, Industrial/Systems Engineering, Statistics, and Applied Mathematics) or in Computer Science or Electrical Engineering is a plus.
- Extensive experience in developing complex computer codes in Python.
- Prior knowledge of surrogate global optimization is an advantage.
Location: Kent Ridge Campus
Department : Industrial Systems Engineering And Management
Employee Referral Eligible: No
Job requisition ID : 6886