Application of Artificial Intelligence (AI) to identify objects and featured associated with them from camera devices
This is a Mitacs project, supported by SOSCIP.
To apply and view all the opportunity details, click here.
Project type: Research
Desired discipline(s): Engineering – computer / electrical, Engineering, Computer science, Mathematical Sciences
Company: MSafe Solutions Corp.
Project Length: 6 months to 1 year
Preferred start date: 05/01/2022
Language requirement: English
Location(s): Toronto, ON, Canada; Canada
No. of positions: 2
Desired education level: Master’s, PhD, Postdoctoral fellow, Recent graduate
Open to applicants registered at an institution outside of Canada: No
Describe the project:
We want to create a technology that will safeguard human lives and save costs to companies by predictable analysis using AI/ML modules.
The project is about developing AI/ML algorithms that capture videos through various camera devices including drones which on injecting/direct streaming analyze the videos:
- Detect Objects (people, flame, fire, distance between objects, smoke, vibrations, non-linear patterns, geothermal emissions, plant survey, 3d mapping, collision alert with moving/static objects)
- After detecting objects analyze the issues visible in an area
- Suggest the necessary changes, send notifications, identify variables
- Generate reports
The candidate will develop features on the existing platform which will complement AI/ML models using machine vision domain by using Tensor/Flow, readily available opensource algorithms, and build on them to improve.
Required expertise/skills:
- Ability to gather information about your topic, review that information and analyze and interpret the information in a manner that brings us to a solution. Research and develop new algorithms based on the problems and standardized methods.
- Strong software skills, be able to apply predictive models and utilize natural language processing while working with massive data sets.
- Machine Learning: Scala, Spark, Tensorflow
- Applied Statistics: Python, R
- Natural Language Processing: Python, R, Java
Computer Vision for Outdoor IoT Product
This is a Mitacs project, supported by SOSCIP.
To apply and view all the opportunity details, click here.
Project type: Research
Desired discipline(s): Engineering – computer / electrical, Engineering, Engineering – other, Computer science, Mathematical Sciences
Company: OtO Inc.
Project Length: 6 months to 1 year
Preferred start date: 07/01/2022
Language requirement: English
Location(s): Canada; Canada
No. of positions: 1
Desired education level: PhD
Open to applicants registered at an institution outside of Canada: No
Describe the project.:
OtO is a connected, outdoor IoT device that helps people spend more time outdoors with the people and pets they love. OtO’s flagship product allows users to treat their lawn and outdoor space with fertilizer, water, a pet waste enzyme, and a natural mosquito and tick repellant.
The future of this product includes integration with a computer vision system to support a wide range of new use cases. The most first use case we want to tackle is “Yardian Mode” – this allows the device to localize and target common pets (raccoons, rabbits, mice, etc) and deter them from destroying gardens and landscaping by humanely spraying them with mild water stream. Future use cases include gamification of the water stream with children and adults (real-time tracking and spraying of people), outdoor activity recognition for predictive applications, and ensuring people or pets are not sprayed when applying solutions/water on a schedule.
The candidate will lead the effort of selecting hardware, developing the algorithms in the prototype, and documenting the materials and methods.
Required expertise/skills:
Computer vision including object and distance recognition, activity recognition, embedded systems.
Field Service Management Machine Learning and Augmented Vision
This is a Mitacs project, supported by SOSCIP.
To apply and view all the opportunity details, click here.
Project type: Research
Desired discipline(s): Engineering – computer / electrical, Engineering, Engineering – other, Computer science, Mathematical Sciences
Company: JBBMobile Inc. (dba ServiceEcho)
Project Length: Flexible
Preferred start date: 05/02/2022
Language requirement: English
Location(s): Toronto, ON, Canada; Canada
No. of positions: 2
Desired education level: Undergraduate/BachelorMaster’sPhDPostdoctoral fellow
Open to applicants registered at an institution outside of Canada: No
Describe the project.:
ServiceEcho is seeking researchers to assist with the development of its Augmented Intelligence for Field Service application. The project will deliver artificial intelligence and augmented reality experience for remote staff and customers in the Manufacturing and Construction industry.
The Field Service Management industry has slowly migrated from paper-based processes to software-based systems. Today FSM and ERP platforms provide a slew of services that make business administration easier. Quoting, Invoicing, Scheduling, Inventory, Payroll, and Reporting are common functions that are streamlined by enterprise software. Despite the Manufacturing and Construction industry’s efforts to adopt FSM and ERP software, customers and field teams are still demanding a better experience when receiving or delivering support or maintenance. Significant resources are wasted when deploying staff to remote customer locations. With little data or insight, complicated workflows become difficult to execute. Lack of data on the issue or customer history with the product also makes communication and planning especially difficult.
An augmented reality application, powered by ServiceEcho’s artificial intelligence engine, will:
- provide a better, on-demand customer support experience
- train field employees more effectively with an AI-powered visual assistant
- use predictive models to suggest maintenance and safety procedures
- deliver efficient and profitable operations through actionable immersive experiences
Successful completion of the ServiceEcho Augmented Intelligence project will provide a unique competitive differentiator for the ServiceEcho FSM platform against its competition.
Required expertise/skills:
Augmented Reality, Computer Vision, Artificial Intelligence, Machine Learning
Leading indicator generation from cryptocurrency to detect fraud
This is a Mitacs project, supported by SOSCIP.
To apply and view all the opportunity details, click here.
Project type: Research
Desired discipline(s): Computer science, Mathematical Sciences, Mathematics, Statistics / Actuarial sciences
Company: Mastercard (AI Garage)
Project Length: 6 months to 1 year
Preferred start date: 05/01/2022
Language requirement: English
Location(s): Toronto, ON, Canada; Vancouver, BC, Canada; Canada
No. of positions: 1
Desired education level: Master’s, PhD, Postdoctoral fellow, Recent graduate
Open to applicants registered at an institution outside of Canada: No
Describe the project.:
The goal is to understand the behavior of cryptocurrency chains when fraud amounts are transferred from the MasterCard network to cryptocurrency exchanges. Based on this understanding we need to come up with an Index score or Leading indicators of certain pattern that indicates high fraudulent activity in the MasterCard network. We need to build such indicators at least aggregate level and verify the importance of the same in predicting future fraud. The algorithm should be able to continue to come up with new patterns and anomalous activity for further analysis.
Required expertise/skills:
Cryptocurrency, Deep learning, Machine Learning, Python, Spark, Big Data analysis, Statistical analysis
Scribble Entity Auto-Resolver – SEAR
This is a Mitacs project, supported by SOSCIP.
To apply and view all the opportunity details, click here.
Project type: Research
Desired discipline(s): Engineering – computer / electrical, Engineering, Engineering – other, Computer science, Mathematical Sciences
Company: Scribble Data
Project Length: 4 to 6 months
Preferred start date: As soon as possible.
Language requirement: English
Location(s): Toronto, ON, Canada; Canada
No. of positions: 1
Desired education level: Undergraduate/BachelorMaster’sPhD
Open to applicants registered at an institution outside of Canada: No
Describe the project.:
The goal of this effort is to design and build a configurable, scalable, automated system for the resolution and linkage of entities spread across disparate data sources. Let’s call this system – Scribble Entity Auto-Resolver (automated resolution and linkage of entities across data sources).
Required expertise/skills:
Expertise in computer science, math, or computer engineering
Knowledge in Python, database design and architecture, data science or data engineering, data privacy
Supply Chain Optimization Using Quantum Computing
This is a Mitacs project, supported by SOSCIP.
To apply and view all the opportunity details, click here.
Project type: Research
Desired discipline(s): Computer science, Mathematical Sciences, Mathematics, Physics / Astronomy, Natural Sciences
Company: ZebraKet Ltd
Project Length: Flexible
Preferred start date: As soon as possible.
Language requirement: English
Location(s): Toronto, ON, Canada; Canada
No. of positions: 1
Desired education level: PhDPostdoctoral fellow
Open to applicants registered at an institution outside of Canada: No
Describe the project.:
We are building novel optimization tools and quantum algorithms for the supply chain industry. We aim to develop demand prediction and optimization tools for the supply chain using quantum computing, annealing, and quantum-inspired technologies.
ZebraKet provides plug-and-play quantum software for optimized inventory stock level recommendations in the supply chain industry. Maintaining the right inventory level and purchasing schedule is critical to reducing tied-up capital, as companies struggle to meet fluctuating demand resulting in surplus or shortage of goods. Our solution intends to provide improved results compared to traditional methods, and faster and cheaper implementation compared to competitors using AI and ML methods.
Required expertise/skills:
- Programming: Python, C++, Java
- Machine learning
- Quantum Machine learning
- Quantum annealing and D-wave hybrid solvers
- QAOA and QUBO problems
- Artificial intelligent
- Mathematical optimization models – Convex optimization
We are looking for an expert in quantum computing (QC) and mathematics. He/she should have rich experience in QC, D-Wave hybrid solvers, and Quantum Annealing. The expert in quantum computing should be familiar with optimization and mathematical modeling techniques. The core responsibility of the expert will be leading the mathematical modeling and development into quantum computing, annealing, and quantum-inspired technologies. Having a good background in quantum machine learning is an advantage.
Universal Graph Embeddings at Mastercard
This is a Mitacs project, supported by SOSCIP.
To apply and view all the opportunity details, click here.
Project type: Research
Desired discipline(s): Engineering – computer / electrical, Engineering, Computer science, Mathematical Sciences, Mathematics
Company: Mastercard (AI Garage)
Project Length: 6 months to 1 year
Preferred start date: 05/01/2022
Language requirement: English
Location(s): Toronto, ON, Canada; Vancouver, BC, Canada; Canada
No. of positions: 1
Desired education level: Master’s, PhD, Postdoctoral fellow, Recent graduate
Open to applicants registered at an institution outside of Canada: No
Describe the project.:
Mastercard is investing heavily in graphs, where the idea is to formulate the transaction ecosystem in its more natural form – as a graph to gain better insights on various entities in the system and to develop new applications which were earlier not feasible with tabular data. One of the major focuses for our team recently has been to develop a universal representation for users which can encapsulate the graphical nature of the transactions and hence provide some additional information over and above raw business-level features. Currently, we have a product already live on user-level called AI Account Intelligent scores which is a suite of scores provided to Issuers (User Bank) to manage the risk their cardholder possesses. We want to generate a universal embedding for all users globally such that they can be used on all these scores to gain a significant uplift over the current production models.
In research, there is a lot of work already done in Graph Learning using GNN’s but the problem of universal embedding has been relatively unexplored. Closed work till now is PanRep (https://arxiv.org/abs/2007.10445) by Amazon. Taking from here and building an approach that transforms well for Mastercard’s transaction behavior is something of a challenging task due to:
- Scalability – Algorithm should be such that we can score on Billion Users.
- Nature of Graph – Transaction graph is significantly different from graphs used in research.
- Universality – Generated embeddings depend immensely on the raw feature set used.
- Inductive – How to handle new users?
- Testing – We would also like to measure for how long these embeddings hold true for and what could be the ways to re-train them quickly.
Required expertise/skills:
Python, Pytorch, DGL, Familiarity with Graph Neural Networks