Senior Research Fellow, Computational Optimization and Software
7 days left
- Full Time
Applications are requested for two Senior Research Fellow positions on computational global optimization or Python Software Development with Prof. Christine Shoemaker, Distinguished Professor in the Department of Industrial and Systems Engineering at the National University of Singapore. (One position focuses on computational surrogate optimization algorithms and a second position focuses on Python programming and software development.)
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).
The initial term of appointment for Senior Research Fellows shall be for at least 12 months, with funds available for extension subject to performance criteria. The remuneration and benefits are internationally competitive, and commensurate with qualifications and experience. Leave and medical benefits will be provided. http://www.nus.edu.sg/careers/whatyougettoenjoy.html.
Review of applications will begin immediately and continue until the positions are filled.
Job applications and inquiries should be send to Prof. Shoemaker at email@example.com. Applicants should include a vitae and indicate desired start time in the email message. Please also put “Job Application-SRF 2020” in the subject line.
The email message can also include any information required to understand how the applicant’s background relates to the qualification requirements listed in this announcement.
Professor Shoemaker (PhD in mathematics, member of the US National Academy of Engineering and Fellow in the: SIAM, INFORMS, AGU, ASCE) was Ripley Professor at Cornell University in US before coming to NUS. She is co-author (with Prof. Bindel and D. Eriksson) of open source surrogate global optimization toolbox “pySOT” in GitHub (which has had over 143,000 downloads. This toolbox provides codes for Prof. Shoemaker ‘s multiple algorithms developed over a decade and makes it easy for users to modify the algorithms). More information about Prof. Shoemaer is given at https://sites.google.com/site/shoemakernusgroup/home.
- For both positions, a PhD degree 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 sufficient. PhD’s from other Engineering areas will also be considered if the applicant also has substantial experience in machine learning computation.
- Extensive experience in developing complex computer codes, preferably in Python.
- Ability to construct and publish papers for leading research journals and conferences. It is an advantage, but not a requirement for a candidate to have expertise in any of the following areas: distributed computing, algorithm proofs, machine learning, surrogates, black-box optimization. Prof. Shoemaker’s group has done work in these areas.
Location: Kent Ridge Campus
Department : Industrial Systems Engineering And Management