The IVADO “Scientist in Residence” program is aimed at startups seeking the scientific expertise of a PhD student in artificial intelligence or data science so as to acquire new skills as well as validate and guide their R&D projects and move them in the right direction.
PhD students taking part in this program acquire hands-on experience in an entrepreneurial environment that is complementary and relevant to their curriculum. This in-company experience enables them to take concrete action in knowledge transfer from academia to industry, and thus contribute to the growth of Québec startups at the leading edge of technological advances in artificial intelligence.
By R&D project, we mean a strategic development project and not a project aimed at rapidly bringing a product or service to market.
Supported fields and examples of mandates
Any project involving artificial intelligence and its applications, for example:
- Business intelligence
- Operations research
- Data science
- Data visualization
- Machine learning
For examples of mandates, here is a summary of some internship offers from previous editions.
Graph-based Generative Architecture
Maket is a platform that allows architects to facilitate the creation and prototyping of new architectural concepts using artificial intelligence. In a few seconds, Maket enables the architect to generate a wide range of new floor plan concepts based on the client’s needs in a collaborative environment to facilitate communication and presentation of the project to the client. Their clients can easily put annotations on the generated plans and collaborate with the architect in a conceptual phase before exporting plans into Autocad.
At the moment, Maket allows architects to generate hundreds of floor plans in a few seconds based on a few client criteria. The current result is an image generated via a generative adversarial network (GAN).
Maket would like to develop a complete generative pipeline that adapts to as many constraints as possible to make a floor plan editable and malleable and thus produce plans that exactly meet the architects’ needs. We are currently working on using graph-based approaches that are based on RNN’s, traditionally reserved for molecule generation.
Our goal is to use this technology to improve the efficiency, sustainability and quality of the architectural design process while mitigating the costs and uncertainty that often accumulate during the process.
Specifically, the goal is to be able to direct our research and development team towards plausible hypotheses and maximize our R&D efforts. Knowledge transfer will allow us to test technical hypotheses rapidly. At the moment, our GAN-based output does not allow us to make a plan scalable and does not comprehensively address the physical and regulatory design constraints that the building must have to maximize its efficiency.
Many of our clients have expressed a desire to not only generate an architectural plan using specific constraints, but also to be able to move rooms and expand the project after our algorithm has generated the plan. The architect will be able to define constraints that will be entered into the system and these will be returned in the form of graphics representing a floor plan that respects the constraints provided. If the architect wishes to iterate on a floor plan that the system has produced, they will be able to modify the constraints (add nodes and change node priorities) and then generate a new graph from the base graph taking into consideration the new updated constraints.
- Graph theory
- Machine learning
- Acrylic Robotics
Machine learning for fine arts
Acrylic Robotics is a robotics startup based in Montreal on a mission to make fine art accessible to the general public. Our technology lets artists make limited-edition painted replicas of original pieces – like a printing press for painted art.
Our motion capture system collects data about each brushstroke and creates a digital fingerprint file that is sent to our painting robots to bring to life, replaying each stroke layer by layer with real paint on canvas. A machine learning-powered optical feedback loop ensures that the robot corrects its mistakes and continuously improves its technique to best match an artist’s style. We’re part of the Next AI Montreal cohort this summer and have previously participated in Centech (Propulsion), Next 36 and the Creative Destruction Lab (AI stream).
Acrylic is looking into the development of a machine learning solution that can accurately estimate the pose of a paint brush that corresponds to an image of a paint stroke. The proposed project will involve the use of reinforcement learning to combine both time series data and imagery to develop a model that can reverse-engineer the motion path of the paintbrush required to create a target brushstroke. This allows us to transition our data collection method away from expensive optical motion capture cameras to normal cameras, allowing us to capture data from artists around the world at a much lower upfront cost.
- Reinforcement learning
- Computer vision
- Movement recognition
Automatic detection of transport modes
Greenplay aims to simplify your active or sustainable mobility initiatives by introducing reliable metrics, fun challenges, and tangible rewards. Our mission is to inspire communities to adopt sustainable behaviors through innovative, reliable and easy-to-use technology. Above all, we want to put people and innovation at the heart of sustainable mobility initiatives
In this project, we will explore new ways to improve our mode (bus, train, metro, car, walking, running, cycling and carpooling) detection tools through innovative technologies and new processes. The goal is to work with our team of developers to implement exploratory methods to increase the reliability of the algorithm and processes.
- Machine learning
- Statistics and data analysis
- Interest in gamification
- Good knowledge of geolocation and sensor data from mobile devices
- Python and Protocol buffer (GTFS)
Objectives, roles and responsibilities
This program aims to promote the transfer of academic expertise to startups. This is achieved through customized training and transfer activities developed to meet the needs of a startup’s R&D project. In this way, the student gains experience that complements his or her academic curriculum, and the startup benefits from this transfer of knowledge, positioning itself as a scientific leader in its field.
Roles and responsibilities
The customized training will be coordinated by IVADO and delivered by a PhD student specialized in the relevant field, in close collaboration with the startup involved. Following are the roles and responsibilities of the various parties.
- Coordination and management of the call for applications issued to startups;
- Receipt, evaluation and selection of applications;
- Communication with and identification of the PhD student interested in the program and the selected R&D projects;
- Administrative and financial management of the program;
- Initial meeting and follow-up meetings during the process to ensure smooth progress and satisfaction of the parties;
- Post-training evaluation and any necessary adjustments to the process.
PhD student / instructor
- Identification of the knowledge transfer goals;
- Identification of the relevant issues to be tackled;
- Identification of and training in available methods and technology tools;
- Validation of the proposed methods and tools;
- Knowledge transfer and support;
- Delivery of the documents developed during the process: e.g. state of the art, documentation on the proposed tools and methods, adoption plan, next steps.
Startup (once selected)
- Identification and mobilization of the R&D team that will lead the project;
- Identification of relevant issues to be tackled during the training and transparency on already tested solutions;
- Availability, transparency and openness to the knowledge transfer to be conducted;
- Before the start of the mandate, payment of a contribution of $2010 plus taxes.
- February 5 to March 10, 2024: call for R&D projects from startups
- Week of March 11: analysis and selection of selected projects
- Week of March 18: 2024: disclosure of results to startups and meeting with selected startups
- From March 18, 2024:
- communication and sharing with doctoral students
- selection and administrative agreement
- payment of contribution by startup and transfer of grant
- From May 1, 2024: start of training and coaching*.
*The start date is determined jointly by the parties.
Knowledge transfer plan
The knowledge transfer plan will cover the 3 components of the program, i.e. preparation and kick-off, training and validation workshops, and transfer. The number of weeks devoted to each stage, as well as the main tasks indicated, may be subject to change depending on the specifics of each project. Following the first stage, a training and transfer plan will be requested.
Given that “Scientist in Residence” is a program for transfer of applied knowledge to an R&D project in artificial intelligence specific to each selected startup, the preparation and startup phase is crucial to achieving smooth progress and the satisfaction of all parties.
We estimate that three weeks (45 h) will be required to complete this first phase. The primary tasks will be structured as follows:
Phase 2: Training and validation workshops
This phase forms the core of the program and, as its name suggests, is the period in which the knowledge transfer will be concretely conducted. Eight weeks (120 h) are set aside for this phase, which will allow time to transfer the theoretical and methodological knowledge as well as the relevant tools for the project, test them in real-world situations, obtain feedback, and make any necessary adjustments.
Phase 3: Transfer and coaching
This last phase consists in ensuring adoption of the best methodologies and tools identified, and completing the knowledge transfer as the R&D project progresses. In addition, the PhD student / instructor will support the startup in defining the next stages of the R&D project. Lastly, this is when the PhD student / instructor finalizes documents benefiting the participating startup at the end of the program (e.g., state of the art, theoretical and practical training, adoption plan). Four weeks (60 h) are planned for this phase.
Amounts, duration and terms of payment
- The program lasts 15 weeks and must be carried out between May 1 and August 31, 2024.
- The PhD student invests +/- 15 hours per week working with the startup.
- Thanks to the funding provided by ScaleAI, the participating startup will be invoiced by IVADO for just 2010$ + taxes.
- The invoice must be paid on receipt, before the start of the project.
- PhD students will receive a $10,000 grant, managed by their home university.
- A maximum of 15 projects will be funded.
For the startup
- Have less than 20 employees;
- Have an R&D team active in the project submitted;
- Be part of or have completed a program with a recognized entrepreneurial support organization (accelerator or incubator) within the last three years;
- Pay the invoice of $2010 + tx upon receipt to IVADO before the start of the course.
For the PhD student
- Be enrolled in a doctoral program at a Québec university;
- Have the support of his/her thesis supervisor;
- Have no employment or equity ties with the startup.
Submitting an application
For PhD students
You can express your interest in participating in the program at any time, simply by writing to email@example.com.
Your email should include:
- If the program is open, a short paragraph describing your expertise, your CV and the startup you would like to support from the list under the Selected R&D projects tab. We will contact you;
- If the program is not open, a short paragraph describing your expertise, your CV and the sector in which you would like to invest. We will contact you once the projects have been selected.
Once the collaboration with a startup has been confirmed, a complete file must be sent to firstname.lastname@example.org, including:
- A cover letter outlining your interest in the research project and the relevance of your expertise to the mandate (max. 1 page);
- Your CV in free format;
- A letter of support from your academic supervisor confirming your expertise in the research project and his or her support for your approach. You can use this template.
For the startup
Complete and submit this form before midnight March 10, 2024. Any submission that is incomplete or sent by any means other than the form will be automatically rejected.
We suggest that you copy and paste your text once written.
If you have any questions, please contact: email@example.com
Evaluation of applications
A committee will evaluate the projects received:
- Two IVADO technology transfer advisors, including the entrepreneurship advisor;
- One person from the research community;
- the Equity, Diversity and Inclusion (EDI) Advisor.
The committee will evaluate applications and make a selection based primarily on compliance with eligibility criteria, the quality of the proposed project and the project’s impact on the company (number of people benefiting from the transfer). The evaluation committee will be multidisciplinary and in line with EDI principles.
Selected R&D Projects
Note regarding intellectual property
As a training and knowledge transfer program, all intellectual property belongs to the startup. If the doctoral student, in conjunction with the company, identifies a new research project and their involvement is desired, then this project must be the subject of a collaborative research agreement separate from this program.
A confidentiality agreement (NDA) may be signed between the doctoral student and the company.
For PhD Student
- Demonstrate integrity and respect in all aspects of your collaboration with the startup;
- Respect the terms of the internship and ensure that objectives and deliverables are met to the best of your ability;
- Mention IVADO in public communications about the project and participate, whenever possible, in IVADO student activities.
For the startup company
- Respect EDI principles when selecting candidates, in accordance with the IVADO EDI reference framework;
- Consult the IVADO brochure on unconscious bias in recruitment;
- Provide a working environment and infrastructure that enables the smooth running and completion of the project;
- Demonstrate integrity and respect in all aspects of your collaboration with the intern;
- Complete the short feedback survey sent by IVADO at the end of the course;
- Mention IVADO in public communications related to work carried out during the program (if applicable).
IVADO’s commitment to equity, diversity and inclusion
To ensure that all members of society benefit equally from the advancement of knowledge and opportunities, IVADO promotes the principles of equity, diversity and inclusion in all its programs. IVADO is committed to providing a recruitment process and research framework that are inclusive, non-discriminatory, open and transparent.