Machine Learning for Internet of Things (IoT)

Beskrivning

Machine learning will be an essential component of the next-generation Internet of Things (IoT) systems, including mobile sensors and wearables. The adoption of machine learning in such systems creates several new opportunities, e.g., real-time and early detection of health abnormalities. However, enabling machine learning in mobile health and wearable technologies also involves several challenges. In particular, such systems are extremely limited in terms of resources (e.g., battery lifetime). 

This thesis project will be jointly done between the Department of Electrical and Information Technology (EIT) at Lund University and Ericsson and will take place in Ericsson at Lund. The main goal is focused on the new generation of machine learning techniques, e.g., brain-inspired algorithms and neuromorphic computing, and to investigate, develop, and evaluate event-driven machine-learning algorithms for resource-constrained Internet of Things (IoT) systems.

Qualifications:
We are looking for a self-motivated and creative student with the following qualifications:
· Master student in the area of Engineering Physics, Electrical Engineering, Computer Science, or similar.
· Fluency in English
· Excellent analytical skills
· Hands-on programming experience (Python)
· Good background in machine learning
· Experience in the development of Spiking Neural Networks (SNNs) is a merit
· Ability to work independently


Should you be interested in this project or would like to know more, please do not hesitate to contact Amir Aminifar at amir.aminifar@eit.lth.se

Kontaktpersoner: Amir Aminifar (EIT)