*Integrating Human and Machine Learning for Enabling Co-Adaptive * *Body-Machine Interfaces* * Sandro Mussa-Ivaldi and Sara A. Solla PI’s * *Northwestern University* We are seeking postdoctoral applicants with interests and experience in computational methods, machine learning, and human-machine interactions to participate in an NSF-funded project. The goal of the project is customizing human-machine interfaces through a collaborative interaction between human and machine learning. This has relevance to the recovery of function after injury to the spinal cord. The same goals apply also to non-clinical domains. Common to the different domains is the necessity to learn novel movement skills. The project will consider how this challenge is met through the interaction with an adaptive interface, the body-machine interface, that maps body motions onto commands to external devices. The working hypotheses are H1) that through a dynamical process of learning the human operators form an internal model of the interface and connected device, and H2) that the human machine interface can be adapted to its users by an update rule that tracks the learning process of the human operator. The success of the research project will lead to a new family of intelligent human machine interfaces capable of customizing themselves to the evolving ability of their users. Applications, including a brief statement, a bio sketch, and the names of two references, should be sent to bomiproject2021@gmail.com