ResCon Technologies employs Machine Learning to create digital twins of complex dynamical systems. Our approach achieves stunning fidelity using minimum power and data, making it compatible with edge computing hardware. This enables model training directly on the edge in real-time without the need for a cloud connection or enormous datasets. Edge-hosted digital twins provide data analysis, adaptive control, and predictive health and status.
July 2022 - ResCon has been awarded multiple Phase I contracts to explore incorporation of its Machine Learning algorithms into edge systems. More information to follow in an official press release later this fall.
July 2022 - ResCon is seeking an individual with a passion for aerospace that has a background in Machine Learning, Software Engineering, and/or Physics to help us research and implement ML-based models on edge hardware.
July 2022 - ResCon has been awarded a Phase II STTR contract through AFWERX to expand on our Phase I with Ohio University to model, control, and predict the health of a full hybrid electric powertrain. Our work will initially concentrate on UAS, with future expansion opportunities for advanced aircraft.
January 2021 - ResCon is seeking a Software Developer/Flight Stack Engineer to assist in our implementation of machine learning and advanced sensor fusion on drones.
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