Recording from the peripheral nervous system can be used to decode control signals exchanged throughout the body, with applications in creating assistive technologies and treating chronic diseases. Our laboratory has collected unique datasets from multi-channel nerve cuff electrodes, which record data from the surface of nerves. We have developed neural networks to decode these recordings by classifying the source of each detected neural event. Using existing data, this project will involve refining neural network architectures and training strategies to optimize performance. Creating neural networks that can generalize well over time and across subjects with minimal re-calibration is of particular interest. The student will have the opportunity to gain a better understanding of real-world data science challenges in neurotechnology, and of strategies to manage these obstacles when developing deep learning systems.