The Department of Computer Science at University of Miami is inviting applications for tenure-track or tenure eligible faculty positions at levels of Associate Professor and Professor. The successful candidates must conduct research in Data Science, including areas such as Machine Learning, Deep Learning, Computer Vision, Cognitive Cybersecurity, Blockchain, Real-time Analytics, Streaming Analytics, Cyber-analytics, and Edge Computing, and are expected to develop/maintain an internationally recognized research program. The selected candidate will be expected to teach classes at the undergraduate and graduate levels. The faculty in these positions will be housed primarily in the Department of Computer Science and will have responsibilities in the Institute for Data Science and Computing (IDSC).

A cross-disciplinary team of researchers from the Universities of Stirling, York, Cardiff, Manchester, and Southampton are working together on an EPSRC-funded project, Edgy Organism, to develop a novel end-to-end neuromorphic design approach drawing inspiration from how data is processed and represented in the brain and build an efficient hardware architecture based on spiking neural networks. The project aims to develop novel computing solutions, that can autonomously and reliably detect illegal or harmful activities in crowded public spaces, with minimum intrusion of personal space and privacy. We are recruiting a team of outstanding researchers from Visual Neuroscience, Psychology, Edge Computing, AI/ML, and Neuromorphic Engineering, to work with us on achieving Edgy Organism project’s ambitious objectives. As part of this project, Psychology, Faculty of Natural Sciences, University of Stirling is offering a fixed term (27 months) full-time Postdoctoral Research Fellow position to work with Dr Elena Gheorghiu and the cross-disciplinary team of researchers.

AI embedded in real systems, such as in satellites, robots and other autonomous devices, must make fast, safe decisions even when the environment changes, or under limitations on the available power; to do so, such systems must be adaptive in real time. To date, edge computing has no real adaptivity – rather the AI must be trained in advance, typically on a large dataset with much computational power needed; once fielded, the AI is frozen: It is unable to use its experience to operate if environment proves outside its training or to improve its expertise; and worse, since datasets cannot cover all possible real-world situations, systems with such frozen intelligent control are likely to fail. Lifelong Learning is the cutting edge of artificial intelligence - encompassing computational methods that allow systems to learn in runtime and incorporate learning for application in new, unanticipated situations. Until recently, this sort of computation has been found exclusively in nature; thus, Lifelong Learning looks to nature, and in particular neuroscience, for its underlying principles and mechanisms and then translates them to this new technology. Our presentation will introduce a number of state-of-the-art approaches to achieve AI adaptive learning, including from the DARPA’s L2M program and subsequent developments. Many environments are affected by temporal changes, such as the time of day, week, season, etc. A way to create adaptive systems which are both small and robust is by making them aware of time and able to comprehend temporal patterns in the environment. We will describe our current research in temporal AI, while also considering power constraints.