Postdoctoral
Research Fellow (Stochastic Modeling, Operations Research) 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, 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,
which naturally includes training in applied probability and optimization
models. Applications from candidates with expertise in applied probability, statistical
machine learning, 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. Applicants should
include a cover letter describing their background, and a CV with specifics on
academic qualifications and technical skill-set. Shortlisted candidates
will be required to arrange recommendation letters from two experts who are
familiar with his/her work. The positions come with a competitive salary
determined based on the selected candidate’s qualifications and experience.
Closing date: March 15,
2021. Applications will be considered as they arrive and the search will be
considered closed immediately upon finding a suitable candidate. |