Grants and Contributions

Our technical challenge is evaluate our ingredients, cooking processes, and packaging in order to pinpoint and eliminate factors that would hinder shelf-life extension and jeopardize sales growth.

Simulating optical components at micro and nanoscale using general purpose 3D optical solvers is often computationally expensive. The computational burden can be reduced significantly if structures in question can be modelled well in 2D. For most nanophotonic structures, however, current approaches to 2D modelling are inaccurate at best and, at worst, completely fail to give even qualitative design guidance. Previous work has demonstrated that machine learning models can create a reliable link between 2D and 3D representations of select grating coupler configurations that include metamaterials. This project expands on that work by offering to build a single joint surrogate model representing multiple grating coupler configurations simultaneously and leverage active learning tools to perform efficient data collection to train such a complex model. The use of the resulting model will be demonstrated in the inverse design of highperformance couplers with improved fabrication robustness and novel dispersion properties

Process engineering to identify gaps in the firm's processes and to develop and implement solutions for improvements to achieve higher outputs and reduced cycle time.

There is a significant demand for algorithms and applications for increasingly complex quantum hardware. Several platforms are currently being explored that rely on various degrees of freedom in quantum systems. Bosonic quantum systems with implementation in superconducting and photonic circuits are based on encoding information in the continuous variable (CV) parameters of the system that are often characterized with quadrature values of a harmonic oscillator. This system is now advancing towards generating fault-tolerant qubits based on the generation of Gottesman-Kitaev-Preskill (GKP) states. The Project will explore two key paths with focus on quantum computation based on CV systems. The first goal is to develop novel optimization algorithms (based on quantum optimal transport theory) to advance photonic circuit optimization tools for quantum computation. The second goal is to develop applications that are naturally suited for implementation on bosonic systems, namely the simulation of vibrational motion in molecules.

There is a significant demand for algorithms and applications for increasingly complex quantum hardware. Several platforms are currently being explored that rely on various degrees of freedom in quantum systems. Bosonic quantum systems with implementation in superconducting and photonic circuits are based on encoding information in the continuous variable (CV) parameters of the system that are often characterized with quadrature values of a harmonic oscillator. This system is now advancing towards generating fault-tolerant qubits based on the generation of Gottesman-Kitaev-Preskill (GKP) states. The Project will explore two key paths with focus on quantum computation based on CV systems. The first goal is to develop novel optimization algorithms (based on quantum optimal transport theory) to advance photonic circuit optimization tools for quantum computation. The second goal is to develop applications that are naturally suited for implementation on bosonic systems, namely the simulation of vibrational motion in molecules.

The identified project focuses on developing a secure, scalable digital platform for both building owners and carbon credit buyers, alongside robust GHG quantification and financial models, to drive sustainable investment in building decarbonization.

The project being worked on is building a fleet assistance system for trucking industry. The outcome from the Fleet Assist project will result in transforming the customers fleet of trucks into smart, connected and AI enabled trucks. This provides an unparalleled user experience for drivers and managers. It goes beyond the basics, providing dynamic data capture for pos-incident data analysis, and delivering real-time insights with advanced fleet tracking and monitoring features. The system seamlessly pairs with existing telematics systems.

The Project will support development of intellectual property capacity within the Firm.

The project aims to address the need to identify threat targets and evaluate appropriate neutralization methods. By addressing these needs, RAPTOR software will be better equipped to protect defended assets against unauthorized drone activity.

With almost $1B in property sales to date, Bode’s scale up strategy further advances its industry first and leading AI enabled platform for buying and selling property online. The project will involve the Customer Success Specialist leveraging key internal systems to further automate listing creation and syndication through AI capabilities while connecting to Bode’s data lake offering key insights to Bode’s large enterprise builder customers and 30,000 Canadian homeowner accounts. This role will also work closely with Bode’s data scientist to ensure the implementation of tools for further enhancements to ensure regulatory compliance.