Excellence Scholarships – PhD

IVADO funding program for excellence scholarships –  PhD

IVADO’s commitment to equity, diversity and inclusion and note to applicants
To ensure all members of society draw equal benefit from the advancement of knowledge and opportunities in data science, IVADO promotes equity, diversity and inclusion through each of its programs. IVADO aims to provide a recruitment process and research setting that are inclusive, non-discriminatory, open and transparent.

Overview

Description

FAQ

Application

Results - 2018 Contest

Program description

  • Field of study: The IVADO Scholarship Funding program supports research on the issues raised in the Canada First funding competition: data science in a broad sense, including methodological research in data science (machine learning, operations research, statistics) and its application in a range of sectors including the priority sectors of IVADO (health, transportation, logistics, energy, business and finance) or any other sector of application (sociology, physics, linguistics, engineering, etc.).
  • Amount of award and grant period: 25k$ per year for a maximum of 12 sessions or 4 years
  • Application deadline: April 22nd, 2019
  • Expected application notification date:  End of June, 2019
  • Criteria: See the description tab
  • Submission: See the submission tab

Program objectives

The goal of the excellence scholarship program is to support promising students in their training as future highly qualified personnel (researchers, professors, professionals) and more generally, future actors in the field of data science, mainly in IVADO members’ areas of excellence: operations research, machine learning, decision sciences.

Eligibility

  • Scholarship applicants must:
    • have already earned their Master degree prior to the date on which they are applying or be enrolled in the last session of the program. IVADO will be flexible with applicants who provide an adequate explanation for a career interruption or particular circumstances. This explanation must be included in the application (e.g. pregnancy/maternity or sick leave);
    • intend to attend HEC Montréal, Polytechnique Montréal, Université de Montréal, McGill University or University of Alberta;
    • have a first class minimum average grade (3.7/4.3 or 3.5/4.00) over the previous years of study.
  • Professor (supervisor) applicants must:
    • hold a faculty position as a professor at HEC Montréal, Polytechnique Montréal or Université de Montréal;
    • Professors at the University of Alberta and McGill University may act as supervisors providing they are full members of one of these research groups: Mila, CIRRELT, GERAD, CERC Data Science, CRM, Tech3Lab, AMII.
    • Only submit one application to the competition.

Funding period

The funding period starts in June 2019.

Amounts and terms

The funds shall be transferred to the office of research of the supervisor’s university, and the university shall pay the postdoctoral researcher according to its own compensation rules. For projects that require ethics approval, the funds shall only be paid out once the approval is granted. Some projects may require specific agreements (e.g. pertaining to intellectual property).

Funding may be cut, withheld, delayed or rescinded under the circumstances outlined in the letter of award.

Competitive process

Review and criteria

The applications shall be reviewed to ensure compliance with program rules (e.g. applications that are incomplete, exceed the page limit or list an ineligible applicant or supervisor). Only the applications that meet all criteria will be forwarded to the review committee.

The parity-based review committee shall be made up of university professors and shall not be listed as a supervisor by any applicant. However, given the small size of the communities in certain areas, it may prove difficult to select expert reviewers who are not included in an application submitted to the competition. In such cases, a reviewer may be required to assess an application despite being listed in another application as a supervisor. An external reviewer may also join the committee. The committee shall ensure by all possible means that the reviewer does not influence the ranking of the application in which he/she is included.

The review committee will check the project’s alignment between the research project and IVADO’s scientific direction, then shall rank the applications based on excellence, as well as the project’s alignment with IVADO’s overarching framework, which aims to promote multidisciplinary collaboration and diversity in data science. In terms of excellence, the committee will specifically assess:

In terms of excellence, the committee will specifically assess:

  • Research ability
  • Depth and breadth of experience: multidisciplinary and professional experiences, extra-academic activities, collaborations, contributions to the scientific community and society as a whole, etc.
  • Expected adequacy with the proposed project

Final step and commitments

The student shall:

  • be physically present at his/her supervisor’s university;
  • contribute to IVADO’s community and activities by, for example, taking part in:
    • presentations on his/her research;
    • training and knowledge dissemination activities;
    • consultations;
    • activities generally undertaken by career researchers (mentorship, assessment, co-organization of events, etc.);
  • recognize that he/she is a member of an academic community to which he/she shall contribute;
  • comply with the Tri-Agency Open Access Policy on Publication. Postdoctoral researchers are encouraged to publish their research findings (papers, recordings of presentations, source codes, databases, etc.), in compliance with the intellectual property rules that apply to their own specific case;
  • recognize the financial support granted by IVADO and the CFREF or FRQ when disseminating the research results and, more broadly, in all the activities in which he/she takes part

The supervisor shall:

  • provide a work environment that is conducive to the completion of the project
  • oversee the work of the student

FAQ

  • Is there a particular format for preparing a CV?
    • No, there is no particular format that needs to be followed. However, each piece of the record must help the assessor to form an opinion on the record. A CV that is too long or confusing may make evaluation more difficult.
  • Are there any specific rules for the recommendation letter?
    • No, there are no specific rules for the recommendation letter.
  • Can candidates send recommendation letters themselves?
    • No, recommendation letters can only be upload by their author in the platform.
  • Can I send my unofficial transcript?
    • No, you must upload us your official transcript including all your current results. Originals or certified copies must be scanned and uploaded to the application and for non-Canadian universities, you must specify the rating scale.

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Application through the platform

Please apply through: https://ivado.smapply.io/

All applications will contain:

  • a questionnaire to be completed on the platform WITH a description of the project (maximum length of one page);
  • CV (free format) to be uploaded;
  • Master transcripts (as well as information on the grading scale when the transcript is issued by a non-Canadian university);
  • recommendations (a maximum of 3), including an uploaded letter directly from the future  supervisor.
  • Chun Cheng (Polytechnique Montréal, Louis-Martin Rousseau)
    • Our project dedicates to deal with uncertainty in drone routing for disaster response and relief operations. To tackle the uncertainties arose from disaster scenarios, like uncertain demand locations and quantities for relief supplies, we use data-driven robust optimization (RO) method. This technique protects the decision makers against parameter ambiguity and stochastic uncertainty by using uncertainty sets. Therefore, it is significant to set proper parameters for the uncertainty set: a small set cannot accurately capture possible risks while a larger one may lead to overly conservative solutions. To address this problem, we use machine learning (ML) technique to extract information from historical data and real-time observations, and create the parameters by ML algorithms. After calibrating the uncertainty set, we will determine appropriate models for the problem by considering various theories in RO, such as static RO and multiple stage adjustable RO. These approaches will be measured against other applicable approaches such as stochastic programming.
  • Dominique Godin (Université de Montréal, Jean-François Arguin)
    • Ce projet de recherche a pour objectif de développer et mettre en application des techniques d’apprentissage machine afin de grandement améliorer l’identification des électrons par le détecteur ATLAS du LHC, le plus grand accélérateur de particules jamais construit et l’un des projets scientifiques les plus ambitieux de tous les temps.Afin de mener à bien le programme d’ATLAS, il est nécessaire d’identifier et mesurer chacune des particules, lesquelles s’y créer à un taux de 40 milliards par seconde et génèrent un flot astronomique de données. Parmi celles-ci, les électrons revêtent une très grande importance, mais ils sont également excessivement rares, ne représentant qu’une infime fraction. Considérant la taille et complexité des données disponibles, le problème d’identification des particules aussi rares que les électrons constitue un terrain d’application idéal pour les méthodes d’apprentissage machine. Les algorithmes actuels d’identification des électrons sont très simples et ne font pas usage de ces méthodes de telle sorte qu’une percée dans ce domaine serait une première mondiale qui pourrait éventuellement paver la voie à des découvertes majeures en physique des particules.
  • Charley Gros (Polytechnique Montréal, Julien Cohen-Adad)
    • Multiple sclerosis (MS) is a disease, with a high rate in Canada, that leads to major sensory and motor problems. This disease affects the neuronal signal transmission in both brain and spinal cord, creating lesions, which are observable on images acquired with an MRI scanner. The count and volume of lesions on an MRI scan of a patient is a crucial indicator of the disease status and commonly used by doctors for the diagnosis, prognosis and therapeutic drug trials. However, the detection of lesions is very challenging and time consuming for radiologists, due to the high variability of their size and shape.This project aims at developing a new, automatic and fast method of MS lesion detection on MRI data of spinal cord, based on newly developed machine learning algorithms. The new algorithm’s performance will be tested on a large dataset involving patients coming from different hospitals in the world. Once the algorithm is optimized, it will be freely available as part of an open-source software, already widely used for spinal cord MRI processing and analysis. A fundamental goal of this project is the integration of this algorithm in hospitals to help radiologists in their daily work.
  • Thomas Thiery (Université de Montréal, Karim Jerbi)
    • When we are walking through a crowd, or playing a sport, our brain continuously makes decisions about directions to go to, obstacles to avoid and information to pay attention to. Fuelled by the successful combinations of quantitative modeling and neural recordings in nonhuman primates, research into the temporal dynamics of decision-making has brought the study of decision-making to the fore within neuroscience and psychology, and has exemplified the benefits of convergent mathematical and biological approaches to understanding brain function. However, studies have yet to uncover the complex dynamics of large-scale neural networks involved in dynamic decision-making in humans. The present research aims to use advanced data analytics to identify the neural features involved in tracking the state of sensory evidence and confirming the commitment to a choice during a dynamic decision-making task. To this end, we will use cutting-edge electrophysiological brain imaging (magnetoencephalography, MEG), combined with multivariate machine learning algorithms. This project, for the first time, will shed light on the whole-brain large-scale dynamics involved in dynamic decision-making, thus providing empirical evidence that can be generalized across subjects to test and refine computational models and neuroscientific accounts of decision-making. By providing a quantitative link between the behavioral and neural dynamics subserving how decisions are continuously formed in the brain, this project will contribute to expose mechanisms that are likely to figure prominently in human cognition, in health and disease. Moreover, this research may provide neurobiological-inspired contributions to machine learning algorithms that implement computationally efficient gating functions capable of making decisions in a dynamically changing environment. ln addition to advancing our knowledge of the way human brains come to a decision, we also foresee long-term health implications for disorders such as Parkinson’s disease.