Design, validation and deployment of very Low-Power Machine Learning models for Smart Glasses: embedding into different hardware platforms and performance comparison. - 2026_CDR_DEIB_3

Scientific-Disciplinary Group

09/IINF-01 - Electronics

Description

The candidate will be involved in the development and implementation of machine learning models on wearable electronic circuits, devices, and platforms, with particular emphasis on smart eyewear. The research activities will address multiple application domains, including embedded electronics, computer vision, audio signal processing, and Human–Computer Interaction (HCI), with specific focus on gesture recognition and multimodal interfaces based on audio signals. These activities will involve the adoption of various neural network architectures, including Convolutional, Artificial, and Spiking Neural Networks and their embedding into electronic platforms such as ARM-CORTEX, RISC-V and others. The candidate will develop the complete model pipeline (training, optimization, embedding, benchmarking) on resource-constrained systems. Furthermore, she/he will also address the mechanical and power constraints of smart glasses, specifically about sensors, electronics, and batteries.

Job posting website

https://www.polimi.it/en/bandi-per-contratti-di-ricerca

Number of positions

1

Funding body

Politecnico di Milano

How to apply

https://aunicalogin.polimi.it/aunicalogin/getservizio.xml?id_servizio=1079

Selection process