Research Fellow in the Interface of Operations Research and Machine Learning
Singapore University of Technology and Design

Research Fellow

Apply now Job no: 494475
Work type: Contract, full-time
Location: Singapore
Categories: PhD, Engineering Systems & Design, Others, Engineering - Others, Others

We invite applications for a postdoctoral fellow position to work in the interface of operations research and machine learning in Singapore University of Technology and Design (SUTD). SUTD is established to advance knowledge and nurture technically-grounded leaders and innovators to serve societal needs, with a focus on Design, through an integrated multi-disciplinary curriculum and multi-disciplinary research. It is one of the six autonomous universities in Singapore.

The post-doctoral researcher will be specifically working on developing data-driven models and methods for tackling tail risks in large-scale decision problems affected by uncertainty. With rapid technological progresses in data acquisition and seamless computation in cloud, data-driven formulations involving large-scale optimization models have become critical for businesses to proactively manage risk and derive value from various analytics endeavours. However, the mere nature of applications we aim to tackle with these large-scale optimization models (think of automated rebalancing of large portfolios, managing omnichannel supply-chains, autonomous vehicles, power distribution networks, etc.) demand that the decisions derived from these automated models are robust, reliable and are not prone to extreme risks due to the presence of uncertainty. Moreover, with modern datasets containing heterogenous subpopulations, the usual practice of minimizing average risk is found to result in optimality (for the whole population average) at the expense of some minority subpopulations suffering unduly high risks. In the context of managing operations, it is natural that fairness-critical settings ranging from smart-cities, healthcare, public-transportation, etc. require uniform performance such that no subpopulation of a specified size in the dataset suffers extreme risks. Motivated by these challenges, the research will focus on developing data-driven optimization modeling paradigm for deriving efficient decision choices which possess controlled low probabilities for resulting in extreme risks.

An ideal candidate will have a PhD with expertise in Operations Research background with familiarity in stochastic modeling and optimization methods. Applications from candidates with expertise in applied probability, statistics, and engineering disciplines which require training in stochastic modeling and optimization are also welcome. Proficiency in programming/scripting and training large-scale machine learning models is an added advantage. 

Applications should contain a covering letter describing their background, a CV with specifics on academic qualifications, technical skill-set and contact information of up to three references. The position is for one year and can be extended to another year based on satisfactory performance. The position comes with competitive salary determined based on the selected candidate’s qualifications and experience. If interested, please send your questions and application to

Applications close: 31 Mar 2021 Singapore Standard Time

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