We would like to encourage potential PhD candidates interested to work on understanding the role of stochastic neuron models in neuromorphic computing to apply for the above PhD studentship. We are including below a non-technical introduction to the project involved. The candidate will be jointly supervised by Prof. Osvaldo Simeone, Professor of Information Engineering, King's College London and Dr. Yansong Chua, Institute for Infocomm Research, A*STAR. For further queries, we maybe reached at: Prof Simeone: osvaldo.simeone at kcl.ac.uk Dr Chua: chuays at i2r.a-star.edu.sg More application details are also available at https://www.kcl.ac.uk/research/funding-opportunities/doctoral-research-oppor... https://www.kcl.ac.uk/research/funding-opportunities/doctoral-research-oppor... For all its recent breakthroughs, modern machine learning based on deep neural networks is becoming increasingly unaffordable in terms of computing and energy resources needed to run training algorithms that achieve state-of-the-art performance. This poses possibly insurmountable challenges for the implementation of efficient learning methods on resource-limited devices such as smart sensors or wearables. A possible solution to this problem is the adoption of the new paradigm of neuromorphic computing, which relies on energy-efficient sparse spike-domain processing and communication that are inspired by the operation of the brain. Whether Spiking Neural Networks (SNNs) can overcome the limitations of conventional deep networks for the implementation of low-power machine learning is a fundamental question that is currently being investigated by major technology companies and universities. In this project, this issue will be tackled both theoretically and through hands-on experiments by leveraging the complementary expertise of the respective research teams at KCL and A*STAR. Specifically, this research will seek to understand whether conventional deterministic models for SNNs can be improved by probabilistic models, which are typically used in neuroscience to model the brain operation, in terms of accuracy, speed, and robustness. In the first two years, at KCL, the project will concentrate on deriving models and learning rules for probabilistic SNNs. In the last two years, at A*STAR, the research will shift to aspects related to implementation, with a focus on the comparison between deterministic and probabilistic SNNs and on the use of nano-scale devices for the implementation of probabilistic SNNs.