Two Postdoctoral Research Associates in Neurorobotics are required for a period of 48 months to work on the Horizon/InnovateUK project “PRIMI: Performance in Robots Interaction via Mental Imagery. This is a collaborative project of the University of Manchester’s Cognitive Robotics Lab with various academic and industry partners in the UK and Europe. PRIMI will synergistically combine research and development in neurophysiology, psychology, machine intelligence, cognitive mechatronics, neuromorphic engineering, and humanoid robotics to build developmental models of higher-cognition abilities – mental imagery, abstract reasoning, and theory of mind – boosted by energy- efficient event-driven computing and sensing. You will carry out research on robot neuro/cognitive architectures, using a combination of machine learning and robotics methodologies. You will be working collaboratively as part of the Cognitive Robotics Lab at the Department of Computer Science at the University of Manchester under the supervision of Professor Angelo Cangelosi.

A Postdoctoral Research Associates in Neuromorphic Systems and/or Computational Neuroscience for robotics is required for a period of 3.5 years to work on the Horizon/InnovateUK project “PRIMI: Performance in Robots Interaction via Mental Imagery. This is a collaborative project of the University of Manchester’s Cognitive Robotics Lab with various academic and industry partners in the UK and Europe. PRIMI will synergistically combine research and development in neurophysiology, psychology, machine intelligence, cognitive mechatronics, neuromorphic engineering, and humanoid robotics to build developmental models of higher-cognition abilities – mental imagery, abstract reasoning, and theory of mind – boosted by energy-efficient event-driven computing and sensing. You will carry out research on the design of neuromorphic systems models for robotics. The postdoc will work collaboratively with the other postdocs and PhD students in the PRIMI project. This post requires expertise in computational neuroscience (e.g. spiking neural networks) for robotics and/or neuromorphic systems.

Azim Premji University has launched an exciting new interdisciplinary major in psychology and cognition, focusing on themes like human cognitive development in the life cycle, mental health and well-being, and machine intelligence and learning. The programme aims to provide holistic insights into the interplay of the mind and behaviour by drawing from various disciplines such as philosophy, neuroscience, psychology, computer science, and socio-cultural contexts. We are specifically looking for faculty who specialise in Developmental Psychology and Cognitive Sciences. Exceptional candidates with expertise in any other related field are also encouraged to apply. We look for applicants who resonate with the purpose of the University and are keen to contribute to the design, development, and delivery of the courses in psychology and cognitive science in our undergraduate programme. The programme is residential for students, and faculty are expected to contribute to research, teaching, and mentoring students, and help build a vibrant community of learning.

The potential of machine learning and deep learning to advance artificial intelligence is driving a quest to build dedicated computers, such as neuromorphic hardware that emulate the biological processes of the brain. While the hardware technologies already exist, their application to real-world tasks is hindered by the lack of suitable programming methods. Advances at the interface of neural computation and machine learning showed that key aspects of deep learning models and tools can be transferred to biologically plausible neural circuits. Building on these advances, I will show that differentiable programming can address many challenges of programming spiking neural networks for solving real-world tasks, and help devise novel continual and local learning algorithms. In turn, these new algorithms pave the road towards systematically synthesizing machine intelligence in neuromorphic hardware without detailed knowledge of the hardware circuits.