COMPREDICT
Omid Charrakh is a Machine Learning Researcher at COMPREDICT since July 2023, following extensive academic experience at Ludwig-Maximilians-Universität München, where a Doctor of Philosophy in Quantum Causal Modeling was obtained in March 2023. Prior roles at LMU included research and teaching positions, focusing on causal modeling, machine learning, and projects such as wildlifeML, a computer-vision initiative. Charrakh's background also encompasses research on thermal noise simulations in high-frequency electronics and contributions to projects involving micro-satellites and robotics at Amirkabir University of Technology, where a Bachelor of Science in Electrical, Electronics and Communications Engineering was earned.
COMPREDICT
COMPREDICT‘s virtual sensors and health & usage monitoring provide unique insights of vehicle usage and behavior without additional hardware for all sectors of mobility. Our solution is currently used in applications with 2 up to 18 wheel vehicles – and even trains. The virtual sensors measure the real dynamic behavior of components likesteering, brakes, powertrain and battery, regardless of whether the vehicle is a prototype or a series product, for a single vehicle or an entire fleet. We close the feedback loop between product development, in-use behavior or fleet management and service operations. With COMPREDICT, optimized component design and maintenance allow our customers to reduce weight, CO2 emissions and environmental impact over the whole product lifecycle.