Innovative Projects Realized

Natural Language Processing (NLP) powered Clinical Documentation

Our company is developing an AI-powered clinical documentation platform. In the proposed project, the intern will assist us in improving our Natural Processing (NLP) models designed to automate clinical documentation for Orthopedic Physical Therapy clinics. These NLP models will be capable of processing text comprising medical vocabulary and terminologies. With this project, we aim to develop more robust, reliable, and high-accuracy language models which meet the expectations and needs of our customers.

Partnership network growth & relationship management

We are designing this project as this year, one of our primary growth strategies is to leverage partnerships to accelerate our entry into global markets. We are looking for an ambitious individual who will lead this crucial operation and help drive this sales channel by creating and managing all international partnerships. This will greatly expand our reach and help introduce our user friendly and affordable software to dealers all over the world. The challenge is to design, test and modify an effective partner success strategy that will strengthen the Glo3D brand while still being scalable as we grow and enter new verticals. We also would like to automate a good chunk of this partner onboarding experience and require someone who can lead this initiative.

Stage en Analyse d’Affaire

Better Being œuvre dans le domaine du bien être corporatif en aidant les entreprises créatives ou Bcorp à améliorer leur culture de mieux être en créant des activités et programmes ludiques et instructifs à l’aide d’une équipe d’expert pluridisciplinaire et une approche holistique. Notre équipe d’experts est constituée de personnes passionnées, créatives et talentueuses (chef, nutritionniste, thérapeute, préparateur mental, kinésiologue et professeur de yoga et méditation) qui ont un intérêt pour le partage de connaissance et la centralisation d’expertise.
Le stagiaire aura pour but de formaliser et standardiser les différents processus d’affaires de l’organisation. BetterBeing est encore une start-up qui a besoin de mieux se structurer afin de faciliter les opérations quotidiennes. Le stagiaire devra alors à l’aide du fondateur cartographier l’architecture d’affaire de l’entreprise et communiquer à toute l’équipe les différents processus mis en place: Processus de vente, de créations d’activité ou de programme, de consultation en entreprise, de recrutement… etc Ce projet est extrêmement important pour l’entreprise car à l’aube de la commercialisation, il est important d’assurer que tout fonctionne au mieux et cela passe par une meilleure compréhension de toutes les étapes à suivre pour assurer la livraison de la valeur du service.

Visualization Platform Development and Optimization

Terris is creating an Earth Intelligence SaaS platform to help solve major challenges that many industries are facing, including the renewable energy sector and emergency response sector. Through this project, the intern will be engaged to take on specific tasks that will help build on the work Terris’ team has been developing. This work will help Terris to advance its’ 3D offerings from the advanced prototype stage to being commercially viable products.

Reinforcement Learning based Graph Convolutional Recommender Systems

This project aims to use and experiment deep learning technique on modern recommender systems such as Graph Convolutional Network. The purpose of this implementation will be to drastically improve recommendation structure’s benchmark. This will allow extract user’s embedding by mapping from pre-existing features that describe the user such as ID and relevant attributes.
In this project students will be integrated as a member of the advanced analytics research team that includes multiple PhD holders in relevant domains.
Students would work on the following main topics:
1. Implementing Graph Convolutional Networks as a recommender system.
2. Implementing an Actor-Critic Deep Deterministic Policy Gradient models for graph-based recommender systems.

Image Processing for Digitalizing Engineering Drawings

This project will focus on digitalizing one specific type of engineering drawing by applying image processing technology. IPS has developed technology to digitalize different types of engineering drawings, and identified different technology and product development needs on a technology road map. This project is established for image processing for piping isometric drawings, and is important to building technology components to address some specific aspects of drawing digitalization (such as lines, curves, and shapes )

Route optimization tool for vessels in ice-covered waters complying with carbon intensity index regulatory constraint

Route planning plays an integral part of each voyage in maritime operations. The selected route must be optimized in terms of economic factors and safety concerns. It also has to adhere to national and international regulations. Recently, the International Maritime Organization introduced a new regulatory instrument to promote the
decarbonization of the shipping industry. The regulatory instrument is called the carbon intensity index (CII). This project aims to solve route planning where all economic objectives are optimized while the operation adheres to the new CII regulation. The proposed method uses an Artificial Intelligence method called Reinforcement Learning
to formulate this issue. In the model, the vessel is an agent that explores the ice-covered environment to search for the best routes. A system of reward signals is defined to make sure the solutions achieve the optimality where their operation meets the CII constraint. This model will be tested in a simulation environment using a realistic voyage scenario in the Canadian Arctic.

Wind turbine interference suppression in HF-radar data

High-frequency surface wave radar (HFSWR) is recognized as one of the essential tools for remote sensing of the ocean surface. HFSWR received data contains valuable information that can be used for ocean wave forecasting, and since it provides real-time data, it can be applied for search and rescue operations, oil and pollution spills, and tsunami detection. When HFSWR is located close to the wind turbine farm, the spinning blades of wind turbines adversely affect the radar received data. In this project, we plan to investigate a real-time technique to estimate the turbine parameters and develop a method to mitigate the interference of the wind turbine in HFSWR received data for an arbitrary number of wind turbines.

Data aggregation, analytics and innovative ML application for Australian Clients-Koan Analytics Inc.

This research project is focused on data analytics for our Australian clients. One, focused on the data aggregation and exploration analytics for the State of Queensland, the other for the State of Western Australia. The interns will be working with cutting-edge machine learning and NLP approaches. This project will help increase the quality and speed of analytic runs we can offer to our clients.

IceCube – Shipborne Data Acquisition system for Arctic Autonomous Shipping

The purpose of this project is to take existing state-of-art machine learning techniques and implement them for ice classification in polar seas. Ice classification plays a critical role in any icebreaker voyage. An ice specialist onboard the icebreaker is required to classify all ice environments encountered. This process is tedious and time consuming. This project aims to automate this process. In using cutting edge neural networks, images taken from aboard icebreakers can be used to classify each pixel in an image giving overall context and information about the environment. These ice classifications would serve to generate important documentation for icebreakers as well as contribute to the formation of ice maps for the Canadian Ice Service.

Hivenue AI Project

We are developing a P2P/AI marketplace, and we’ll integrate a matching process to let roommates match between them, or hosts to find the ideal tenant, based on different criteria, like lifestyle, interests, backgrounds, university, neighbourhood, space / time, reviews, market predictions, etc.

The candidate will do research development and will apply knowledge of advanced analytic algorithms and technologies (e.g. machine learning, deep learning) to deliver better predictions and/or intelligent automation.

The candidate needs to be familiar with programming languages related to the development of solutions using artificial intelligence in a computer vision context, and aware of deep learning platforms and/or libraries.

Machine Learning on Large Data Streams for CLS Operations

The CLS control system for the particle accelerator and beamlines, which are larger than the size of a football field, generates over 600,000 digital data streams to monitor and control the facility 24/7. The task of monitoring and reacting to these data streams is largely up to the Operators and other Egineers, Physicists and Scientists who manually check the data with the assistance of pre-set thresholds that generate alarms if limits are reached. Often once an alarm is triggered it is too late – the facility has dropped out of operations or equipment has been damaged. This project aims to take the first steps into a new paradigm of predictive maintenance and performance monitoring, by building modern tools to analyse the data for trending and correlations between data streams before an alarm limit is reached. The present method of manual human analysis is very time consuming and the CLS does not have the resources to retrospectively put all their data streams through algorythms that perform the same expert analysis provided by an expert human. This project aims assist the experts and provide a tool to analyse the huge data stream for possible correlations between the data streams that will lead to predicting common failure modes and prevent them from happening.