Advanced and automated feature engineering for food and agriculture industry applications - ON-955

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.: 

This initiative is centered on the development and commercialization of innovative feature engineering techniques, tailored specifically for the Food and Agriculture sector. The scope of this project encompasses both static and dynamic imaging modalities to ensure comprehensive applicability.

The project's objectives include:

1. The enhancement of feature extraction processes, aiming to capture the most informative and discriminative attributes from images of food items, whether stationary or in motion.
2. The meticulous assessment of these features in terms of their predictive power and utility in subsequent analytical tasks, ensuring that they contribute meaningfully to the performance of predictive models.
3. The augmentation of the feature space where necessary to improve model robustness, which includes generating synthetic features or modifying existing ones to better capture the complexity of food quality attributes.

Additionally, the project aims to automate these stages to increase efficiency, reduce time-to-market for new analytical solutions, and elevate the standard of data processing in the food industry. Through these technological advancements, the project aspires to set new benchmarks for accuracy and speed in food quality assessment and monitoring.

Expertise ou compétences exigées: 

Feature Engineering, Machine Learning, Computer Vision