This project builds on proven success of offline system behavioural analysis where pre-trained AI/ML models successfully predicted anomalous system this project we plan to deploy previously studied AI/ML techniques and algorithms on embedded devices. These models will run in real-time continuously monitoring and analysing system behaviour to anticipate possible anomalies and automatically trigger suitable recovery actions to ensure system stability.
Currently pursuing Master/PhD in Computer Science Artificial Intelligence Data Science or similar.
Proven software engineering skills in complex multi-language systems ideally proficient in Python and C.
Proven expertise in machine learning with a passion for data-centric ML.
Proven expertise in anomaly detection using AIML algorithms
Proficiency in Python and C ideally embedded C
Basic Embedded SW and Real-Time OS (RTOS) concepts
Familiarity with Large Language Model (LLM)
Demonstrated problem-solving aptitude and strong learning agility with a proactive mindset and eagerness to quickly adapt to new technologies and concepts.
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