The Rehab Neural Engineering Labs (RNEL) are looking to hire a post-doctoral associate to work on basic science and translational research projects occurring as part of an ongoing clinical trial of intracortical brain-computer interfaces to restore upper limb function. RNEL is a collection of labs working to achieve the same goal: To improve the quality of life of individuals with neurological impairments by advancing scientific understanding of motor and somatosensory systems to engineer new rehabilitation therapies and technologies. RNEL has active collaborations across the University of Pittsburgh, Carnegie Mellon University, and more broadly in academic and industry.
The candidate would have the opportunity to contribute to one or more NIH-funded projects. The first project will study bidirectional cortico-cortical communication within human sensorimotor cortex. We know that humans can perform a range of static and dynamic grasping movements with a high level of skill, yet it is unclear how cortex achieves this flexibility in control. We will study how population dynamics in motor cortex change with behavioral context and how they are shaped by sensory feedback. Through this project, we hope to gain a better understanding of how motor cortical activity generalizes across static and dynamic behaviors as well as the potential to drive plasticity within cortical circuits that communicate sensorimotor information, which has relevance for understanding skill learning and improving rehabilitation after injury.
The second project aims to improve the robustness of brain-computer interface performance to support the translation of this technology out of the lab and into the home. We will study how the physical or cognitive state of a participant impact neural activity and system performance. In addition, we will characterize the extent to which experience- or training-induced motor learning can drive neural plasticity and improve BCI robustness.
Responsibilities include the design, execution, and management of experiments within the context of a human brain-computer interface project. The candidate will apply machine learning techniques to quantify population-level neural activity patterns to address scientific questions related to sensorimotor control, motor learning, and/or context-dependent neural activity.
The candidate should have a PhD in Biomedical/Neural Engineering, Neuroscience, Rehabilitation, Statistics, or a related field.
For Info:
Please contact Jen Collinger for more information: collinger@pitt.edu