Post-Doc in Digital Design For Efficient Embedded Machine Learning Processors

We want to extend our team with a post-doctoral researcher on resource-efficient digital implementations or machine learning processors, focused around deep learning.

Recently deep neural networks, such as convolutional neural networks (CNNs) or long short-term memory (LSTM) networks have gained enormous popularity in the signal processing community. In the micro-electronics research domain this has sprouted attention on customized processors for efficient embedded deep neural network inference. Our team has published several of these state-of-the-art processors over the past few years.

We will build upon our work in the field of deep learning processors and hardware / algorithm co-optimization, which has demonstrated to save orders or magnitude on energy efficiency. Yet, we want to extend this work towards a.) New application domains (such as acoustics, depth sensing and sensor fusion); b.) online learning for user customization; c.) run-time adaptivity for increased efficiency.

In this project we closely collaborate with researchers from KU Leuven's algorithmic machine learning group, researchers from Imec, from various international research groups and several partner companies.

The Post-doc will have ample opportunities to interact with top research institutes and important industrial players in the field.

• Candidates must hold a PhD degree in Electrical Engineering (or equivalent), with a background in digital design or processor design. 
• Additional research / educational experience in computer architectures, machine learning and chip tape are a strong plus. 
• We are looking for a team player with the capability to work in an international research team. 
• Excellent proficiency in the English language is also required, as well as good communication skills, both oral and written

• An exciting research environment, working on the intersection between emerging research domains (machine learning; processor design; ultra-low power chip and system design)
• The ability to interact closely with our industrial partners, as well as many academic research partners in ongoing projects
• A thorough scientific education, the possibility to become a world-class researcher 
• A KU Leuven affiliation, one of the largest research universities in Europe
• The possibility to participate in international conferences and academic as well as industrial collaboration