Synthetic data and data augmentation for Agri-Food data systems - ON-953

Project type: Research
Desired discipline(s): Engineering - computer / electrical, Engineering, Computer science, Mathematical Sciences
Company: Anonymous
Project Length: 4 to 6 months
Preferred start date: As soon as possible.
Language requirement: English
Location(s): Stoney Creek, ON, Canada; Halifax, NS, Canada; Toronto, ON, Canada
No. of positions: 1
Desired education level: Postdoctoral fellowRecent graduate
Open to applicants registered at an institution outside of Canada: Yes

About the company: 

A multinational food technology company with headquarters in Stoney Creek, Ontraio. The company provides AI powered technology for companies across the food and agriculture supply chain.

Describe the project.: 

The project seeks to spearhead the development of synthetic data generation techniques and the automation of data augmentation processes for the food and agriculture industry.

The main objectives are:

1. Synthetic Data Generation: Innovating methods for creating high-quality, synthetic datasets that can supplement real-world data, thus enhancing the depth and breadth of training datasets for machine learning models.
2. Data Augmentation Automation: Designing and implementing algorithms to automate the augmentation of data, thereby increasing the diversity and volume of datasets without compromising on quality, which is critical for robust machine learning model training.
3. Commercial Application Research: Translating the latest advancements in data augmentation research into commercial applications specifically tailored for the food and agriculture sector, ensuring that the solutions developed are practical, scalable, and have a direct impact on industry challenges.

This project will enable the creation of enhanced machine learning models that are more accurate, generalizable, and effective in addressing the unique challenges faced by the food and agriculture sector, particularly in quality assessment, prediction, and management systems.

Required expertise/skills: 

Data Science, Data Augmentation, Machine Learning, Computer Vision