Target group

The IVADO “Scientist in Action” program is aimed at startups interested in hosting a master’s-level intern to move forward with an applied research project in artificial intelligence.

The master’s student participating in the program will complete a credited internship as well as gain entrepreneurial experience and explore new career opportunities.

Objectives

The program objective is to match talents to the entrepreneurial ecosystem. This is achieved by means of a credited applied research internship at a startup company. The candidate acquires experience in the entrepreneurial environment and is in a better position to make career choices. For the startup, the program offers a unique opportunity to access sought-after expertise at an affordable cost, while promoting its organizational culture and potentially recruiting a new employee.

Supported fields and examples of mandates

This program is designed to support research in the areas delineated in our CFREF funding proposal: data science in the broad sense, encompassing methodological research in data science (machine learning, operations research, statistics) and its applications in multiple sectors, including our priority sectors (health, transportation and logistics, energy, business, and finance) and any other sector of application (e.g., sociology, physics, linguistics, engineering).

Any project pertaining to data valorization, e.g., in:

  • Business intelligence
  • Operations research
  • Data science
  • Data visualization
  • NLP
  • Machine learning
  • Etc.

Without limitation, internship projects may focus on the following:

  • Contribution to the improvement of processes involved in data collection and data quality;
  • Implementation of measures for monitoring performance indicators (KPIs);
  • Creation of modern, practical and efficient visualization tools (Power BI: dashboards, reports);
  • Improvements to existing BI reports and dashboards;
  • Harnessing of available data to meet business needs;
  • Development and execution of database queries (SQL);
  • Ensuring data integrity, including extraction, storage and processing;
  • Understanding of the needs of a business and users so as to provide them with appropriate data solutions;
  • Assessment of opportunities for use of artificial intelligence;
  • Design of tools and models to serve as a basis for decision-making;
  • Formulation of forecasting models in a business context (e.g., purchasing, sales, cancellations, unsubscribe requests, outages);
  • Customer segmentation in a marketing context;
  • Optimization of inventory management;
  • Modelling of credit risk;
  • Analysis of unstructured data (text) in a sentiment analysis context;
  • Etc.

Student profile

Although the program is open to all students enrolled in Campus Montréal institutions (Université de Montréal, Polytechnique Montréal, HEC Montréal) who meet the eligibility criteria and demonstrate the abilities required for the project, the main academic programs from which the students will be drawn are as follows:

HEC Montréal

Polytechnique Montréal

Université de Montréal

Calendar

  • November 30, 2021, to January 19, 2022 (noon): call for internship projects issued to startups;
  • Week of January 19, 2022: analysis and selection of projects;
  • Week of January 24, 2022: communication with the chosen startups;
  • February 10 to March 3: applications open to students
  • Beginning May 1: start of internships.

Amounts, duration and terms of payment

This is an applied AI research internship program lasting six months, full-time, beginning May 1, 2022.

Ten scholarships of $15,000 each will be remitted to the student interns.

A 25% contribution ($3,750 plus taxes) is required from each participating startup. This contribution will be invoiced by Mitacs and is payable before the start of the internship. IVADO will contribute another 25% ($3,750 plus taxes) and Mitacs will complete the funding with a 50% contribution ($7,500).

Eligibility criteria

For the applicant

  • Be enrolled in an MSc program at one of the Campus Montréal universities (Université de Montréal, Polytechnique Montréal, HEC Montréal);
  • Register the internship as part of their course load (supervised project or credited internship);
  • Submit an application following the home university’s standard process.

For the startup

  • Have been in operation for less than five years;
  • Have a qualified person on staff to host the intern;
  • Be part of a recognized entrepreneurship support organization or have completed a program within the past two years;
  • Have annual sales of less than $1 million;
  • Pay a contribution of $3,750 + taxes to Mitacs before the start of the internship.

Submitting an application

For the applicant

The list of available internships and the information pertaining to each application process will be available below.

For the startup

Complete and submit the form before noon on January 19, 2022. Submissions that are incomplete or sent by any means other than the form will be automatically rejected.

List of available internships

1. Cloud Conseils

AI-powered SaaS Cybersecurity chatbot

CloudConseils is a startup that offers next-generation cloud security solutions and cloud consulting services. We are focusing on the development and migration of cloud applications and SaaS in AWS and Azure.

The main part of the internship is the implementation of an AI conversational agent using Natural language processing and reinforcement learning algorithms to help the non-expert cybersecurity employees in order to fix the detected vulnerabilities during a chat session.

The intern will participate in the overall process of machine learning (data collecting, data cleaning, choosing models, training, benchmarking, improvement, etc.) using Python libraries, Azure Cognitive Services or AWS Sagemaker. The intern will also deliver technical reports and proof of concepts.

This internship is available for students of:

2. Ohmic.ai

AI for Extraction of Biomedical Signals from Headphones

Ohmic is a company aiming to provide physiological and activity based information through headphones. We can achieve biometric sensing through technology that can be added as a dongle for wired headphones, or integrated into a wireless earbud design.

In the current context of making headphones smarter, the intern student will be researching and developing the best machine learning strategies that can be used to extract and classify biometric features from headphones. The role of this internship is to help us understand the needs for current and future developments of smart headphones from R&D prototypes to mass market products.

You will be:

  • Working on a literature review and comparison table of machine learning techniques for biometric signals, highlighting their respective advantages and drawbacks.
  • Developing AI algorithms for feature extraction of biometric signals, such as heart rate, heart rate variability, respiration rate, and in-ear canal characterization.

To do so, the student will search for published works, i.e, academic papers, on the topic of AI for biometric signal extraction in wearables, and use MATLAB or Octave as a numeric computing environment to implement those algorithms.

This internship is available for students of:

3. AIMH

AI for Mental Health: Developing tools to help clinicians streamline treatment for children with mental health difficulties

AIMH (AI for Mental Health) is a start-up that uses AI methods to support mental health. We have developed the prototype of a platform that can be used as a decision support tool, in clinical practice of childhood psychiatric disorders. Our initial focus is ADHD (Attention-deficit/ Hyperactivity Disorder). Core of this technology is the combination of machine learning with knowledge of underlying biological processes. The developed methodology facilitates a closer collaboration between families and mental health professionals to provide improved personalized care for the individual. The internship will provide support for the development of tools to help clinicians treat mental health difficulties.

This internship is available for students of:

4. Lerna

Federated Learning on Sensitive Mobile Data (Python/Java)

Are you interested in working on the forefront of privacy-preserving machine learning, designed for the post-cookies world?

Are you keen on experiencing the high-impact environment of an early-stage VC-backed startup?

At Lerna AI we are building a federated machine learning platform for mobile applications. Our technology enables mobile-first companies to learn about their app users without invading their privacy. We predict the user behavior based on their context, demographics, mood, etc. in order to identify best timing for out-reach, without retrieving any data!

Our novel architecture speeds up the learning process by more than 50x, rendering it practical for real-world mobile set-ups, by running the whole ML process on thousands of mobile phones in a distributed fashion.

You will be:

  • Discovering the benefits and limitations of federated learning and finding ways to overcome the latter
  • Working on mobile sensor, activity, and app data to enable ML on disparate and skewed data sources
  • Selecting and fine-tuning ML algorithms that are suitable for our application, with an efficient federated learning version, in Python or Java, and
  • Even building federated algorithms from scratch!

This internship is available for students of:

5. Latence Technologies

Real-time AI-based analytical decomposition of 5G network latency

LatenceTech offers a cloud analytics and monetization solution for cellular networks with a special focus on ultra-low latency connectivity. Using SAAS and AI, our solution helps mobile operators, telecom vendors and advanced industries to track, predict and secure the new benefits of 5G cellular technology.

The project consists in performing an analytical decomposition of the response time latency of 5G cellular technology. This will allow a better understanding of the reason for the high variance in milliseconds of the 5G latency by analyzing, in real time, the sub-components of the 5G latency such as transmission time, propagation time, routing time, etc. This data will allow customers (mobile operators) to take palliative and preventive measures to improve the quality of the 5G network in terms of latency. Understanding the components of latency will help to “understand why and how” the network generates such latency and why it varies over time.

This internship is available for students of:

6. Blindsight Therapeutics

Optimizing visual perceptual learning

Blindsight Therapeutics is a digital therapeutics startup specializing in vision rehabilitation. Our first product, the Blindsight Trainer, is a treatment for cortical blindness –   a visual deficit where you permanently lose up to half your visual field as a consequence of damage to your visual cortex. We combine insights from artificial intelligence and neuroscience to teach these patients to see again.

This internship will be supervised by the CTO of Blindsight Therapeutics, Patrick Mineault. Dr. Mineault received his PhD in neuroscience from McGill in 2014, and is an expert at the intersection of AI and neuroscience. His work has been published in NeurIPS, PNAS, Neuron, and the Journal of Neuroscience. In addition to his academic background, Patrick has industrial research experience in some of the biggest tech companies in Silicon Valley, including as software developer and data scientist at Google, and as research scientist at Facebook.

This internship will support our research efforts into visual perceptual learning. Our goal is to understand how healthy subjects can learn tasks rapidly and robustly. This will teach us valuable insights into how learning can be optimized in patient populations. As part of this project, the student will implement an analysis platform and analyze human perceptual data, supporting Blindsight’s mission to help patients.

This internship is available for students of:

7. LivingSafe

Improve and develop the decision making algorithms and data quality of the LISA solution (internship in French)

Technologies LivingSafe is developing an intelligent monitoring system, using radar technology and artificial intelligence, for use in the care units of seniors’ facilities and in home care. The solution, called LISA, consists of a wall-mounted device placed in the resident’s unit that collects and analyzes data to detect incidents (falls, lack of movement, wandering, etc.) and track the resident’s health status. The device is paired with a platform used by caregivers to receive alerts and statuses, thus improving the care provided to seniors while respecting their dignity.

The internship will focus on developing and improving the accuracy of the algorithms/models used for the different functionalities of LISA. As part of a pilot project, prototypes are currently being installed in care units to acquire data and test the algorithms used by the LISA system. The data collected by the devices will be used to experiment with changes to the system parameters in order to improve its performance.

In order to optimize the data transfer speed and internal communication of the prototype, improve the quality of the raw data and increase the accuracy of the LISA system’s functionality, the intern will be asked to analyze the collected data to extract as much metadata as possible, i.e.:

  • revising the hardware communication protocols (radar and microprocessor) used in LISA devices,
  • working on the pre-processing layers that bridge the gap between the raw data and the decision making algorithms
  • examine the decision making algorithms to increase the accuracy of the system and
  • Analyze the collected data in a real environment to extract as much metadata as possible.

This internship is available for students of:

8. Vope Medical

Constant clear vision in minimally invasive surgery (MIS)

Vope Medical delivers constant clear vision in minimally invasive surgery. Vope’s AI-driven software optimizes the lens cleaning process and minimizes disruptions through automation. This allows the surgeon to stay completely focused on the procedures they are performing, eliminating distractions that arise from the current lens cleaning process. The intern will develop deep learning (DL) computer vision systems to model microsurgerical contamination. This includes de-fogging the surgical view, quantifying contamination levels, classifying contamination type, and segmenting contaminants. The ideal candidate would be well versed in Python development, especially with the Numpy, Pandas, cv2, Tensorflow, and Keras packages. They would also have experience in developing software that models real-life phenomena and the machine learning pipeline, from data procurement, to pre-processing, to model deployment. Furthermore, they will have experience working in a lean team using Agile development practices. It is also preferred that they have experience in computer vision applications of artificial intelligence and image processing techniques.

This internship is available for students of:

9. Mely.AI

Development of an AI model to facilitate data extraction from forms

At Mely.ai, we are changing the supply chain and logistics landscape by providing the industry with AI-assisted tools. We want to provide an unprecedented experience to our users with the latest technologies to better serve their customer.

Mely.ai’s current value-add is to extract data from forms based on specific templates.  We also have a method to extract data without a template, but it lacks precision.

The project is therefore to create a generic template that can better extract data from a form into structured data.  Using computer vision techniques, we will be able to identify the tabular data as well as the available key values.

The main responsibilities of the trainee will therefore be:

  • To analyze the current methods of the company in order to make a report
  • Propose an alternative solution to the current method of information extraction
  • Implement the solution in proof of concept mode
  • Deployment of the solution in the SaaS platform of Mely.ai.

This internship is available for students of:

10. Berkindale Analytics

Analysis and forecasting of market conditions

Berkindale Analytics is a cloud-based company that wants to create a next-generation platform to provide both market data and packaged analytics to financial clients via direct download, interface, and dashboards accessible from a web portal. Berkindale Analytics is distinguished by its service arm that engages in data discovery, new analytics design, and ETL (“Extract, Transform, and Load”) configuration with client data. The Berkindale team is composed of financial data analytics specialists and financial modeling researchers. Interns will benefit from the expertise of the Berkindale team throughout their internship.

The general objective of the project is the development of a cloud-based platform for the ingestion and normalization of financial data and the calculation of market analytics. More specifically, in the context of this internship, the student will be required to develop supervised and unsupervised machine learning tools adapted to capture stylized facts of a market or a trading platform, such as liquidity and volatility indicators.

This internship is available for students of :

Evaluation of applications

For evaluation of the applied research internship project applications submitted by the startups, a committee comprising the three MSc internship academic representatives of Campus Montréal (Université de Montréal, Polytechnique Montréal, HEC Montréal), a representative of the entrepreneurship support organizations ecosystem and the IVADO Entrepreneurship Advisor will be formed. Compliance with the eligibility criteria, the quality of the proposed internship project and the project’s impact on the company will be the main criteria evaluated.

If there is more than one applicant interested in a project, an interview will be arranged with the company, which will make the final choice.

Note regarding intellectual property

All intellectual property belongs to the startup. A non-disclosure agreement (NDA) may be signed by the successful applicant and the company.

Commitments

  • Applicants
    • Show integrity and respect in all exchanges with the startup;
    • Mention IVADO in all public communications about the internship (e.g., postings on social platforms) and take part, when possible, in its various student activities.
  • Startup
    • Provide a work environment conducive to completion of the project;
    • Show integrity and respect in all exchanges with the student;
    • Mention IVADO in public communications relating to the intern’s work.
  • Our commitment to equity, diversity and inclusion
    • To ensure all members of society draw equal benefit from the advancement of knowledge and opportunities in digital intelligence, we promote principles of equity, diversity and inclusion across all of our programs, and we commit to providing a recruitment process and research setting that are inclusive, non-discriminatory, open, and transparent.