Postdoctoral Scholarships

Postdoctoral Scholarships Program

Introduction

Description

FAQ

Application

Results - Winter 2018 Contest

Program description

  • Program Name:  IVADO funding program for postdoctoral scholarships
  • Program Type: Postdoctoral scholarships and support to postdoctoral activities
  • Type of research: fundamental or partnership
  • Strategic / priority domain: Data science, data-driven innovation.

Program goals

The goals of the postdoctoral scholarship program are to:

  • train future researchers and professors and, more generally, future players in the field of data science, mainly in IVADO members’ areas of excellence: operations research, machine learning, decision sciences.
  • promote mobility, attraction and retention of young researchers.
  • for grants in partnership: encourage the development of advanced collaborative/applied research.

Deadlines

Winter 2017 competition:

  • Application deadline: November 15, 2017
  • Expected application notification date: December 22, 2017
  • Official acceptance from the candidate before January 8th, 2018
  • Funding start date: between December 15, 2017 and April 31, 2018

Summer 2018 competition

  • Application deadline: May 15, 2018
  • Expected application notification date: June 15, 2018
  • Official acceptance from the candidate before July 1st, 2018
  • Funding start date: between June 15, 2018 and October 31, 2018

Domains

The IVADO funding program for postdoctoral scholarships supports research concerning the issues described in the Apogée/CFREF grant

http://www.cfref-apogee.gc.ca/results-resultats/abstracts-resumes/competition_2/universite_de_montreal-eng.aspx: data science in the broadest sense, including methodological research in data science (machine learning, operation research, statistics) and their applications in various fields (including health, transportation, logistics, energy, business and finance).

Types and amounts of postdoctoral scholarships

There will be three types of scholarship:

  • Regular scholarships are 70k$/year (including social benefits/taxes), for up to two years, fully funded by IVADO.
  • Partnership scholarships are 70k$/year (including social benefits/taxes), for up to two years, half funded by IVADO and half by a partner.
  • Fellow scholarships are 90k$/year (including social benefits/taxes), for up to three years fully funded by IVADO. In addition, fellow postdocs will have access to up to 10k$ for relocation costs, and up to 15k$ per year for funding their research activities. Candidates must explicitly request to be considered for this type of scholarship. If they are not selected as fellows, the evaluation committee might recommend a regular or partnership scholarship.

Additional research grant for fellows

Fellow postdoctoral researchers will have access to research funds. They will be required to write a short proposal describing their needs and the required budget. The postdoctoral researcher can apply to this program multiple times until the limit of 15k$/year is reached.

The funds will be transferred to the Research Office of the university of the supervisor, and, depending of their rules, the funds might be managed by the postdoctoral researcher him/herself or by his/her supervisor. The postdoctoral researcher will have to provide a financial report.

Allocation duration

For two years maximum and three for Fellows. Funding for the scholarship will be automatically unlocked for the second year at the request of the supervisor and subject to production of a report.

Eligibility, evaluation and funding

Eligibility

For the postdoctoral candidate:

  • Having received his first doctorate for less than five years at the time of application or anticipate to get it by the date of the announcement of results.
    • IVADO will be flexible for the candidates justifying career interruptions and special circumstances.

For the professor submitting the application (supervisor):

  • Being a professor from one of the institutions of Campus Montréal: HEC Montréal, Polytechnique Montréal or Université de Montréal.
  • Professors from the University of Alberta and University McGill can also be supervisors, provided that they are also members of one of IVADO’s research groups ((MILA, CIRRELT, GERAD, CERC Science des données, CRM, Tech3Lab).
  • Should not be the director or co-director of the candidate as a PhD student.

Activities planned for the duration of the scholarship must be aligned with the goals of IVADO and Apogée/CFREF.

Assessment Criteria

  1. Project quality (50%)
    1. Originality, anticipated impacts
    2. Relevance to the goals and domains of IVADO and Apogée/CFREF
    3. Feasibility, relevance of the host laboratory and supervisor
  2. Candidate’s record (40%)
    1. Research contributions
    2. Quality of the PhD thesis and academic excellence
    3. Depth and breadth of experience (multidisciplinary, professional experience, extra-curricular activities, etc.)
    4. Adequacy of experience with the proposed project
  3. Suitability of the project within the overall framework of IVADO and Apogée/CFREF (10%)
    1. Promoting multidisciplinary collaboration
    2. Diversity

Application

Instructions for completing the application are available under the Application tab.

The application is presented by the supervisor. Each supervisor cannot present more than 3 candidates.

Evaluation Process

Proposals will go through an administrative screening to check for program requirements (incomplete, too long, ineligible supervisor or candidate, etc.). Only applications meeting the requirements will be forwarded to the evaluation committee.

The evaluation committee will consist of faculty from Campus Montreal, knowledgeable about areas of excellence of IVADO, and who present no candidate. Due to the small size of the community in some domains, it might be difficult to find relevant reviewers who present no candidates. In such situation, a reviewer might be enrolled despite presenting a candidate, or an external reviewer will be enrolled. The committee will ensure by all traditional means that this reviewer will not be in position to influence the ranking of his own application.

Based on the evaluation criteria, the evaluation committee will conduct a ranking for the candidates asking for the fellow scholarship, and award them if they judge they reach the required level of excellence. Candidates who have not been awarded a fellow scholarship, will then be ranked with all the other applicants. The committee will recommend for each candidate either:

  • Regular funding
  • Partnership funding
  • No funding

The choice of partnership funding will be based primarily on the ranking (excellence of the assessment criteria) and secondly on the potential for applied research.

The applicant then has one month to formally accept the funding. At his official acceptance, the candidate has the opportunity to change the start date, which must take place no later than six months after the announcement of results.

For candidates who have been awarded a partnership scholarship without asking for it in their application, the time to accept will be extended to 6 months to give the opportunity to find a suitable partner. IVADO will provide some support and guidance.

Choice in the application form Possible decision Acceptance Start
Regular Regular One month Six months
Partnership Six months Six months
Rejection n/a n/a
Partnership Partnership One month Six months
Rejection n/a n/a
Fellow Fellow One month Six months
Regular One month Six months
Partnership Six months Six months
Rejection n/a n/a

Funding conditions

The funds (scholarships and potential relocation repayments) will be transferred to the Research Office of the university of the supervisor, and the university will pay the postdoctoral researcher according to its own rules. If research projects require an ethical certificate, funding may be withheld until obtaining it. Some projects, especially in partnership, might require a specific intellectual property agreement.

The funding may be reduced, withheld, delayed or canceled in some cases explained in the Award letter. 

Commitments

The postdoctoral researcher agrees to:

  • Be physically present at his supervisor’s university, or share his time if in a partnership situation;
  • Attend and participate in community/IVADO activities, including for example:
    • presentations on their own research
    • training or knowledge dissemination activities
    • counseling
    • support on various activities normally part of a research career (mentoring, participation in reviews, co-organizing events, etc.)
    • supervision of students, interns, etc.
  • Recognize that he/she is part of an academic community, to which he/she will contribute;
  • Be subject to the policy of the three organizations on free access to publications (http://www.science.gc.ca/eic/site/063.nsf/fra/h_F6765465.html).They are encouraged to publish as much of their research productions (publications, recording oral presentations, source code, databases, etc.) in compliance with the rules of intellectual property that apply in their specific case;
  • Acknowledge the support of IVADO and Apogée/CFREF in the dissemination of research results and more generally in the various activities that the postdoctoral researcher will participate in.

The supervisor commits to:

  • Providing a work environment that suits the completion of the project;
  • Supervising the postdoctoral researcher.

Final report

At the end of the funding, the supervisor and postdoc must submit a final report. Details are explained in the award letter. For Fellow scholarships, the report include  in particular, a financial report, and for partnership scholarships, explicit section of report on partnership and approval of the report by the partner.

The supervisor cannot apply to other funding opportunities from IVADO as long as this report is due.

Guidelines

Generally, the Tri-Agency Financial Administration Guide (http://www.nserc-crsng.gc.ca/professors-professeurs/financialadminguide-guideadminfinancier/index_eng.asp) and the rules of the Apogée/CFREF program (http://www.cfref-apogee.gc.ca/program-programme/admin_guide-guide_administration-eng.aspx) will serve as guides for the program.

Contacts

  • Any questions concerning the funding program can be addressed to:  programmes-excellence@ivado.ca.
  • Please also consult the FAQ section in this page.

FAQ

Questions? Please submit them to programmes-excellence@ivado.ca

The elements of the application are:

  • This form, filled;
    • with a choice between:
      • Regular application (default choice)
      • Partnership application
      • Fellow application
  • CV of the candidate;
  • Ph.D. official grade transcripts (with rating scale for non Canadian universities);
  • Description of the project (max. one page).
  • A maximum of three support letters including at least one from the supervisor and one from the PhD advisor.
  • Confirmation letter from the partner (if the candidate and the supervisor explicitly want a partnership scholarship)

The application must be sent to programmes-excellence@ivado.ca, CCed to the supervisor, as a single pdf file.

 

  • Behrouz Babaki (Polytechnique Montréal, Gilles Pesant)
    • To turn the ever increasing amounts of data into social and economic value, two tasks need to be performed: 1) extracting knowledge from the data, and 2) incorporating this knowledge in the operations that drive the society. The machine learning community addresses the first task by extracting the knowledge from the data and capturing it into ‘learned models’. The second task is studied by the operations research community under the label of ‘optimization’. However, these techniques have been developed almost independently. This makes it less straightforward to integrate them and turn the knowledge obtained from a learned model into actionable decisions. In this project, we exploit the fundamental similarities between the two tasks to develop an integrated system that performs both tasks together. We apply our system to problems in business and finance and demonstrate how this approach can help players in these sectors to use their data for improving their operations.
  • Maxime Laborde (McGill, Adam Oberman)
    • This research is focused on using mathematical tools to accelerate the training time of Deep Neural Networks (DNN)s. DNNs are a powerful tool in Artificial Intelligence, behind applications in machine translation, image recognition, speech recognition and other areas. However training the DNNs requires huge computational resources, which is costly both financially, and in the human effort required to implement them. This research will use advanced mathematical tools to improve the time required to train DNNs.
  • Tien Mai (Université de Montréal, Teodor Gabriel Crainic)
    • This project deals with the planning of intermodal rail transportation, integrating methodologies from operations research and machine learning in a new and innovative way. Intermodal container freight transportation is the backbone of international trade and supports a large part of Canadian and North-American imports and exports. Canada has one of the largest rail networks in the world and Canadian railway companies are both network and terminal operators. They face many large-scale optimization problems that are complex because of their sheer size and the uncertainty that affects planning and operations on a continuous basis. The project focuses on a tactical network load and block planning problem that involves decisions related to blocking and railcar fleet management. Assuming that the train schedule is given, the problem entails three consolidation processes: assignment of containers to railcars, of railcars to blocks and of blocks to trains. The project will be dedicated to designing a service network design model and associated solution method that allows to solve realistic, large scale, instances.
  • Abbas Mehrabian (McGill, Luc Devroye)
    • When designing a machine learning algorithm, it is crucial for the designer to understand the input data to which this algorithm will be applied. It is well known that real-world data for any task has a lot of structure, exploiting which allows for faster learning and more accurate prediction. However, understanding this structure is a highly nontrivial task, given the high dimension of the data. In this project we propose to develop a mathematical framework for learning the structure hidden in the data, via the lens of probability theory. Assuming the data is generated by some stochastic process, we would like to infer its distributional properties. Then a natural question is, which distributions are harder to learn, and which ones are easier. The aim of this project is to answer this question from statistical and computational perspectives, at least for a variety of commonly used classes of distributions, such as mixture models and graphical models.
  • Patrick Munroe (Polytechnique Montréal, François Soumis)
    • Gestion en temps réel du cargo aérien. Le projet à moyen terme est le développement d’un système de gestion du cargo dans les compagnies aériennes en commençant avec Air Canada. Ce système traitera la planification stratégique, tactique et l’opération en temps-réel. Le niveau stratégique évalue des scénarios à long terme sur l’organisation du réseau, les marchés à développer, les alliances à conclure. Le niveau tactique optimise le choix des itinéraires entre chaque paire de villes pour une semaine type d’une saison. Durant l’opération, les vendeurs pourront obtenir en ligne le meilleur itinéraire pour acheminer une nouvelle commande et le prix de revient. À chaque niveau de décision, il faut estimer la demande pour l’horizon considéré et optimiser l’acheminement de cette demande dans le réseau de transport comprenant des avions tout cargo, l’espace disponible dans les soutes des vols passagers, des sous-contrats avec d’autres transporteurs aériens et routiers. La recherche portera sur le développement de nouvelles méthodes d’estimation de la demande et d’optimisation de l’acheminement dans un grand réseau.
  • Maria Isabel Restrepo Ruiz (Polytechnique Montréal, Nadia Lahrichi)
    • The main objective in using optimization approaches for demand and supply management in home healthcare is to match supply and demand by influencing patients/caregivers choices for service time slots/working shifts. Our aim with this project is to develop a decision support tool to deal with approaches for demand and supply management in home healthcare. Specifically, we will implement stochastic models to forecast future demands and to predict caregivers’ absenteeism. Then, we will design and develop choice models to consider patient and caregiver choice behavior. These models will predict the probability of choosing a particular alternative from an offered set (e.g. visit time slots, working shifts) given historical choice data about an individual or a segment of similar individuals. These models will be embedded into an optimization approach that will compute a time slotting/scheduling plan or a pricing strategy to optimally balance the allocation of cost-effective schedules to caregivers and the improvement of service quality.
  • Anne-Lise Saive (Université de Montréal, Karim Jerbi)
    • Every day, we experience thousands of situations, but we only remember few of them. Episodic memory is the only memory system that allows people to consciously re-experience past experiences and it is the most sensitive to age and neurodegenerative diseases. It is thus critical to better understand how to enhance learning and memory in both healthy and clinical populations. Emotions are known to robustly strengthen the formation of long-term memories. Characterizing the influence of positive emotions (joy, happiness) on memory could be pivotal in improving memory therapies, yet the underlying brain mechanisms are still surprisingly misunderstood. In this project, we will use a fully data-driven approach to identify the key neuronal processes strengthened by positive emotions that distinguish events we will durably remember from events we will forget. We will combine for the first time high spatial and temporal resolution brain imaging techniques and state-of-the-art machine-learning algorithms. This will be achieved by assessing the ability of multidimensional (across space, time and frequency) arrays of brain data to predict future memory accuracy.
  • Rabih Salhab (HEC Montréal, Georges Zaccour)
    • Ride-sharing services such as Uber, Lyft or Didi Chuxing match a group of drivers providing rides with customers through an online ride-sharing platform. This business model faces a number of fundamental challenges. Indeed, the drivers considered as independent contractors choose the area they wish to serve, if they accept or reject rides, and when they start and stop working. With no direct control over the drivers, the ride-sharing platform can only use incentives and select the information it provides to drivers and customers in order to improve the quality of service and balance supply and demand. This project aims to develop a model that anticipates how the drivers respond to provided information, which is a combination of request statistics, prices at various locations and times, and estimation of the state of the road network. Moreover, it intends to generate location and time-dependent pricing schemes and optimal information filters in order to optimize the efficiency of the system. For example, the filters control the amount of information to release to drivers about the requests in order to balance the supply and demand and avoid the drivers from deserting some areas.
  • Kristen Schell (Polytechnique Montréal, Miguel Anjos)
    • Hydro-Québec is geographically well positioned to make significant profits in neighboring electricity markets. Facing political mandates to retire coal and nuclear power plants, the markets of Ontario, New York and New England are under increasing stress to provide stable, baseload electricity production. We will utilize the vast historical data from these markets to model their future evolution. Using the insights obtained from this analysis, we will be able to determine optimal strategies for Hydro-Québec to maximize its profits through targeted investment decisions in market interconnections. The results will be generalizable to other provincial utilities in Canada and their participation in the relevant electricity markets.
  • Jean-François Spinella (Université de Montréal, Guy Sauvageau)
    • Acute myeloid leukemia (AML) is the most common form of leukemia in adults. Despite advances in supportive care to treat therapy-related complications, the majority of AML patients will not exceed the two-year survival mark because of relapse. This dismal outcome reflects the sub-optimal treatment orientation of poorly understood subtypes of AML. To improve the treatment and outcome of patients, Dr. Guy Sauvageau and colleagues initiated in 2009 the Leucegene project which has become an internationally acknowledged leader in genetic and biological characterization of AML. Exploiting the most innovative technologies, this program already allowed the sequencing of 452 primary human AML specimens. While several types of genomic alterations have been explored in AML, some of them, such as modifications to the chromosome structure, remain elusive despite their known importance in cancer. We are convinced that this is due to unsuitable analysis and we propose here an innovative machine learning approach to efficiently identify these modifications. Tests will be carried out on our sequenced AML specimens. Ultimately, the method will be released to help the scientific community to exploit its cancer data. From a biomedical point of view, it will allow for better definition of AML subgroups, as well as an increase in the chances of identifying new markers for this disease. With the goal to accelerate the transfer of new knowledge from the laboratory to the bedside, this project will help ensure the correct classification and treatment of AML.
  • Yu Zhang (Université de Montréal, Pierre Bellec)
    • To understand brain mechanism of cognitive functions is the ultimate goal of neuroscience studies, which also provides fundamental guidance for developing new techniques in artificial intelligence. With accumulated evidence in animals and humans, functional dynamics is suggested to be the non-stationary nature of cognitive process. In this project, we aim to apply deep learning models to characterize the spatial and temporal dynamics of BOLD signals at rest and during cognitive tasks. To account for the temporal dependence of MRI signals, a convolutional recurrent neural network is first used to characterize the spatial and temporal dynamics of resting-state data, and then to map the dynamic somatotopic maps during movement of tongue, hand and foot. The model is further adjusted for classification of functional dynamics among multiple task conditions. The derived characteristic functional dynamics, including sequential temporal response functions and corresponding activation patterns, reveals the dynamic process of human cognitive function and provides essential guidance for brain simulation. Furthermore, our proposed method could also be used in clinic applications, for instance searching for temporal and spatial biomarkers for Alzheimer’s disease and evaluating the treatment effects of precision medicine.