Research Scientist, Machine Learning (PhD)
Synaptrix Labs, New York City
What you will do
- Develop new machine learning methods for modeling high-dimensional neural and behavioral data, spanning representation learning, generative modeling, sequence modeling, latent-variable models, and learned dynamical systems.
- Learn latent structure and dynamics from noisy, non-stationary, partially observed time-series data.
- Develop approaches to neural decoding that generalize across people, sessions, tasks, and recording conditions.
- Explore problems at the intersection of deep learning, dynamical systems, system identification, control, information theory, optimization, and statistical learning.
- Investigate self-supervised and unsupervised learning methods that can take advantage of large quantities of neural data without requiring dense behavioral labels.