Grants and Contributions

Collaborate with clinical research teams on verification and validation for real time motion management (RTMM) for the device, and incorporate prototype design modifications to prepare for advanced clinical trials in multiple regulatory jurisdictions. Build another prototype to accelerate testing throughput.

To advance and modernize energy management and performance contracting to make it accessible to small and medium-sized businesses.

REDspace White-label Streaming SDK is a client-side, multi-platform, highly modular and customizable software that incorporates common features found in top streaming apps. The SDK is intended to have the flexibility to support the demands of our clients across an array of innovative features. The SDK is currently intended to target Apple and Android mobile and TV platforms.

This project aims to increase top-of-funnel marketing leads and Marketing Qualified Leads (MQLs) while minimizing customer acquisition cost (CAC) and maximizing visitor-to-MQL conversion rate through vertical-specific targeting and a/b testing.

This project develops DUET (DUal-Sided Energy Tracer) core software engine from a research-focused desktop application to a highly-scalable cloud application and product suite. Enurgen’s software products solve the complex challenges of modeling bifacial utility-scale solar power plants faced in the industry today, from design & optimization through to operations and asset management.

Develop a secure ecosystem for water treatment telemetry, control, and management.

The project includes updating all of our quality assurance processes for software development.

The project will support development of intellectual property capacity within the firm.

Digital twin technology is used to model, explain, understand and predict behavior of complex systems in order to make their performance more reliable or to provide an in silico platform for high throughput testing of the effect of different interventions. Models combined under a digital twin umbrella can also be used to study mechanistic phenomena of a disease. While there are extensive data using genetically and environmentally manipulated laboratory mice to model human disease, these data are study-dependent and have yet to interrogate how metabolism dictates outcome and modulates genetic susceptibility. Moreover, there are no in silico models that combine the multiple behavioral outcomes of these in vivo modelling data with the metabolome. The lack of a murine digital twin is a major missing link in intelligent design of therapeutic strategies for complex diseases. The driving hypothesis of this project is that a digital mouse developed through optimized AI approaches and extensive, available biological data can provide a framework to support the design of evidence-based therapies for neurodegenerative diseases. The main outcome of this project will be an in silico reconstruction and simulation of the dependence of behavior, as a measure of neurodegeneration, on the lipidome and its application in corrective intervention development. The proposed digital mouse will be built using novel AI algorithms and unique, extensive, and longitudinal murine datasets (from birth to death) made available through collaboration.

The goal of this project is to develop an interactive platform for treating sleep problems in shift workers, as well as to integrate portable devices for the development of a learning retention program, and to study the feasibility of estimating and using EMA (Ecological Momentary Assessments) related to circadian rhythms.