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*Post-doc position on memristive neural networks*

Deadline: April 3, 2018
PostDoc (# positions: 1)

The energy efficiency of computation in the brain relies on its unique architecture with high parallelism and sparse memory/computing elements with no physical separation. This is enabled by resistive switching devices, such as the resistive switching memory (RRAM), the phase change memory (PCM), and the magnetoresistive memory (MRAM). These memristive devices can play the role of computing nano devices and plastic synapses in a neural network. Our group is currently developing spiking neural networks with memristive device, where the device physics enables learning and other computing primitive of the brain.

This open postdoc position aims at the study of brain-inspired devices and spiking neural networks. RRAM devices will be fabricated in the clean room Polifab, while the neural networks will be designed and developed on printed circuit board (PCB) with discrete elements. After the electrical characterization, various spiking neural networks will be developed with memristive devices connected to a microcontroller serving as input/output hub. The objective of the work is to demonstrate spiking neural networks for neuromorphic computing with synaptic time dependent plasticity. Results of the testing will serve as input for the development of integrated circuits for brain-inspired in-memory computing.

The gross salary will be 28,000 EUR/year. The position is for one year and renewable. For more information contact