Topic: model generalization

Four-month, full-time research fellowship developing machine-learning infrastructure and reinforcement-learning methods. Projects can address training performance, distributed systems, synthetic-data and environment pipelines, or model generalization. Fellows receive researcher mentorship, a weekly stipend and benefits. Applications are reviewed on a rolling basis for the next cohort, expected in January 2027. San Francisco workspace access and remote participation from the United States, United Kingdom or Canada are available. A subsequent permanent role is not guaranteed.

Electrical stimulation of a neural population may enable direct experiments relating local population activity with subsequent neural or behavioral processes. However, current stimulation approaches do not attempt precise control of population activity, in part due to the difficulty of characterizing diverse neural responses to varied stimulation patterns. We address this gap by building deep network models that extract neuronal spiking responses through stimulation and predict population responses to arbitrary spatiotemporal stimulation trains. To assess the tractability of training accurate