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

Genre de projet: Recherche
Discipline(s) souhaitée(s): Génie - informatique / électrique, Génie, Informatique, Sciences mathématiques
Entreprise: Anonymous
Durée du projet: 4 à 6 mois
Date souhaitée de début: Dès que possible
Langue exigée: Anglais
Emplacement(s): Stoney Creek, ON, Canada; Halifax, NS, Canada; Toronto, ON, Canada
Nombre de postes: 1
Niveau de scolarité désiré: Recherche postdoctoraleNouvelle diplômée/nouveau diplômé
Ouvert aux candidatures de personnes inscrites à un établissement à l’extérieur du Canada: Yes

Au sujet de l’entreprise: 

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.

Veuillez décrire le projet.: 

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.

Expertise ou compétences exigées: 

Data Science, Data Augmentation, Machine Learning, Computer Vision