Research Scientist – Efficient machine learning for time-series data


This is a research scientist position, expected to conduct research on efficient machine learning algorithms for time-series data on edge devices and implement such algorithms on edge hardware, for projects in digital agriculture – eGrazor, digital manufacturing – FDMF/Maven, and smart building/digital twin domains. Specifically, the position will develop lightweight ML models to analyse multi-modal time-series data from multiple edge devices and sensors such as temperature/humidity, or accelerometer, to understand/classify the context in which a sensor device operates, or identify activities of the target object.

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