Postdoc Efficient learning in hardware for neuromorphic computing
Postdoc Efficient learning in hardware for neuromorphic computing
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Job description
Eindhoven University of Technology, Departments of Mechanical Engineering and Electrical Engineering and Eindhoven Hendrik Casimir Institute, has a vacancy for a
Postdoc 'Efficient learning in hardware for next-generation neuromorphic computing and smart sensors'
Neuromorphic computing could address the inherent limitations of conventional silicon technology in dedicated machine learning applications. Recent work on large crossbar-arrays of two-terminal memristive devices has led to the development of promising neuromorphic systems. However, delivering a parallel computation technology, capable of embedding artificial neural networks in hardware, remains a significant challenge. Organic electronic materials offer an attractive alternative and can provide neuromorphic devices with low-energy switching and excellent tunability. Here we propose a novel implementation of backpropagation in hardware enabling - for the first time - tuning of multilayer hardware neural network, an essential step for energy-efficient and edge computing systems such as smart sensors.
Key objectives and scientific challenges
Postdoc 'Efficient learning in hardware for next-generation neuromorphic computing and smart sensors'
Neuromorphic computing could address the inherent limitations of conventional silicon technology in dedicated machine learning applications. Recent work on large crossbar-arrays of two-terminal memristive devices has led to the development of promising neuromorphic systems. However, delivering a parallel computation technology, capable of embedding artificial neural networks in hardware, remains a significant challenge. Organic electronic materials offer an attractive alternative and can provide neuromorphic devices with low-energy switching and excellent tunability. Here we propose a novel implementation of backpropagation in hardware enabling - for the first time - tuning of multilayer hardware neural network, an essential step for energy-efficient and edge computing systems such as smart sensors.
Key objectives and scientific challenges
- Realisation of a neuromorphic array of optimised organic neuromorphic devices
- Demonstration of novel implementation of weight tuning in hardware
- Development of a modular neuromorphic chip with error backpropagation in hardware
Specifications
- max. 38 hours per week
- Eindhoven View on Google Maps
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Eindhoven University of Technology (TU/e)
Requirements
We are looking for a candidate with a demonstrated expertise and background in electrical engineering or circuits, artificial intelligence, microsystems engineering or other relevant discipline. Given the multidisciplinary character of the proposed research the ideal candidate has experience in device physics, circuits, sensors and artificial intelligence and machine learning. Furthermore, the candidate has a hands-on attitude, experimental experience and can work independently as well as collaborate with others.
Embedding
The postdoc will be supervised by prof. Yoeri van de Burgt, associate professor in Neuromorphic Engineering and prof. Marco Fattori assistant professor in Integrated Circuits, both part of the Eindhoven Hendrik Casimir Institute.
Embedding
The postdoc will be supervised by prof. Yoeri van de Burgt, associate professor in Neuromorphic Engineering and prof. Marco Fattori assistant professor in Integrated Circuits, both part of the Eindhoven Hendrik Casimir Institute.
Conditions of employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
- Full-time employment for 2years.
- Salary in accordance with the Collective Labour Agreement for Dutch Universities, scale 10.
- A year-end bonus of 8.3% and annual vacation pay of 8%.
- High-quality training programs on general skills, didactics and topics related to research and valorization.
- An excellent technical infrastructure, on-campus children's day care and sports facilities.
- Partially paid parental leave and an allowance for commuting, working from home and internet costs.
- A TU/e Postdoc Association that helps you to build a stronger and broader academic and personal network, and offers tailored support, training and workshops.
- A Staff Immigration Team is available for international candidates, as are a tax compensation scheme (the 30% facility) and a compensation for moving expenses.
