domain of Operations Research, as well as in the intersection between
Operations Research and Machine Learning. These positions aim at advancing
our collective capabilities to solve hard optimization problems and our
understanding of how external data sources and real-time data can be used
to improve the decision-making process. Earliest starting dates are
September 2017 and January 2018.
* Research Topics
Research topics may be aligned with the interests of the students. Several
pre-defined research projects are also available (some of them in
collaboration with industrial partners). Topics may generally include the
following:
- Large-scale optimization for dynamic (combinatorial) planning problems
(e.g. Benders decomposition, Column Generation, Lagrangian Relaxation)
- Optimization under uncertainty (e.g. stochastic programming, robust
optimization, improvement of uncertainty sets and scenario generation
through external data)
- Improvement of (à priori and real-time) planning through integration of
external data sources via machine learning
- Development of Operations Research based algorithms to efficiently solve
machine learning problems
Research may be fundamental, applied, or a mix of both. Applications may
include domains such as Logistics and Transportation (e.g., vehicle sharing
systems, facility location, traffic management), Telecommunications and
Revenue Management (e.g., assortment planning).
* Research supervision and environment
The students will work under the supervision of Dr. Sanjay Dominik Jena,
professor at the Department of Management and Technology (
mantech.esg.uqam.ca) at the Management School of Université du Québec à
Montréal (École des Science de la Gestion, UQAM). Sanjay Dominik Jena is
also member of the worldwide renowed, Montreal-based research center
CIRRELT (www.cirrelt.ca) and the Canada Exellence Research Chair in Data
Science for Real-time Decision-making (www.cerc-datascience.polymtl.ca).
Students may be co-supervised by other members of these research centers
and will be registered either at the joint Ph.D. program in Management
(including ESG UQAM, HEC Montréal, McGill and Concordia) or at the
respective university of the other supervisor(s).
Montreal is a dynamic Metropolis, located in the province of Québec,
Canada. The city is bilingual (French/English) and known for its
international atmosphere. Montreal is known for its vibrate research
environment, hosting five major universities and several research centers
in the domains of Operations Research and Machine Learning. The successful
candidates will be working in one of the above-mentioned research centers.
* Candidate profile
Students are expected to carry out top-level research. They will develop
efficient solution algorithms to difficult planning problems such as those
in the above-listed domains. Candidates should have a Masters degree (or
equivalent) in Computer Science, Operations Research, Applied Mathematics,
or a related field. They should demonstrate good programming skills.
Experience in mathematical programming, machine learning, or data analysis
is an asset. Candidates should possess a good level of written and oral
English. Even though they may eventually learn French during their stay,
French knowledge is not obligatory.
* Application procedure
Interested candidates should send their application package electronically
to Sanjay Dominik Jena (sanjay.jena@cirrelt.ca). The application should
contain the following documents:
- Cover letter explaining the motivation to perform a Ph.D. in one of the
domains mentioned above in Montreal, your research interests, as well as
your preferred starting date
- Detailed curriculum vitae with list of publications (if any)
- Grade records of Bachelor and Master programs
- Reference letters or contact details of references (one of them should be
the supervisor of the candidate's Master studies)
If you have further questions regarding these positions, possible research
topics, etc., please contact Sanjay Dominik Jena (sanjay.jena@cirrelt.ca).
Positions will remain available until filled.
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