Eco-responsible parking lots technical monitoring project in Quebec

A significant part of urban areas are used by parking spaces that facilitate single car use while contributing to heat island effects and rainwater management challenges. Traditional parking management strategies have not been able to address the environmental and quality of living aspects in cites. The main purpose of this project is to provide a state-of-the-art review of eco-responsible parking lots and analyze their usage and environmental impact.

Project Blinkem - Optical channel communication for Mixed Reality Systems

Project Blinkem is a novel approach to visible light communication, which aims to create a low-cost, scalable, and secure framework using existing technologies. The project proposes encoding data into Infrared LEDs, which can visually communicate with high-resolution image sensors on HMDs as well as a high-speed optical module. By leveraging the security benefits of visible light communication, Project Blinkem offers an inherently more secure method of communication, which can be valuable for a range of applications, including virtual reality environments and the internet of things.

Machine Learning for Speech Enhancement

The core value of Nureva is to provide reliable and easy-to-use audio-conferencing products that offer a good user experience and maximize productivity. The unique Microphone Mist™ technology unlocks new possibilities to pick up audio from anywhere in the room. One subtopic of this research is to investigate ML as it relates to sound event localization and detection so we can track when and where a person is talking in the room. As a result, this can allow virtual microphones to be activated near the talker while attenuating noises in the room.

Software System Error Detection and Resolution

The software development process is a lengthy process and an area where most companies spend a great amount of capital. Approximately half of this time developers are spending on fixing bugs in their code. Faulty software is difficult to identify both in location and reason. After finding a bug, it takes even more time to identify the correct solution to the problem. In this internship, we propose to create a novel approach that identifies topics and relationships between bugs in code.

Blockchain-based ESG and SDG rating platform

Our interns will work on an innovative platform that rates companies based on their environmental, social, and ethical performance (ESG) and their contributions to global goals (SDG). They will help create digital tokens (NFTs) that ensure the data stays secure and trustworthy, while also protecting privacy with advanced technology (ZKSnarks). The project involves developing AI algorithms that can review company reports and generate scores, along with another AI that can interact with investors in real-time.

AI Based Script Builder for Web Payments

To conduct research and provide a feasible solution to create an AI-based script builder to automate the company’s pay-by-web transaction process. The pay-by-web transaction process includes many steps. These are steps such as extracting data from different sources and monitoring email inboxes as well as verifying payment information. The process also involves identifying the correct supplier websites and submission of the complete payment transaction. The project aims to provide an AI script that automates this process to make it more streamlined and efficient.

Using multi-modal data and self-supervised approaches for machine learning in healthcare

This research project aims to address the growing interest in predicting clinical outcomes using machine learning
(ML) approaches applied to Electronic Medical Record (EMR) data. The primary objective of this study is to
develop representations of both EMR and text data found in medical notes using current state-of-the-art ML
techniques. In particular, this research proposes to leverage self-supervised learning techniques to learn dynamic
representations. By doing so, the research aims to improve the prediction of clinical outcomes.

Development of a distributed framework for deep learning models

Layer 6 powers the AI use cases for a variety of banking and financial applications at TD Bank. The goal of the research project is to improve the AI engine by having the training more efficient and distributed among a variety of clusters. The AI engine will allow models to be trained faster and with more optimal performance of the models. An improved AI engine can help deliver better machine learning models to over 25 million customers that rely on TD Bank for their financial decisions.

Command and Control Automation and Reporting

A red team is a group of cybersecurity experts who are tasked with simulating real-world attacks on an organization's systems and networks. They do this by using a variety of tools and techniques to identify vulnerabilities and weaknesses in an organization's defenses. This project implements command-and-control infrastructure, which is critical for the red team or simulated attackers to remotely control systems compromised by them and receive stolen data from the compromised systems.

NEGF based Cryogenic MOSFET simulation including inelastic scattering

The project aims to develop a state-of-the-art numerical simulator to compute transistor’s physical behaviors at deep cryogenic temperatures. First of its kind, the simulator will incorporate physical effects critical for transistor’s operations at cryogenic temperature such as inelastic scattering, while maintaining computational efficiency and robustness. The successful outcome will provide the research community a widely desired tool for understanding and predicting how realistic MOSFETs behave under deep cryogenic temperatures.