Jobs

Our group studies how complex vocal and sensorimotor behaviors are learned, from the computational principles that govern learning to the social interactions through which behavior develops. We combine experiments in songbirds, computational modeling, longitudinal multimodal recordings, and the development of new machine-learning methods for animal behavior.

Our research focuses on how songbirds acquire, refine, and organize their vocal repertoire through auditory experience and practice. We study computational principles of learning, including motor exploration, intrinsic reward, and reinforcement learning. We are also interested in vocal learning and social communication in groups, using longitudinal recordings of audio, video, and wearable sensor data. In parallel, we develop machine-learning methods for multimodal signal processing, individual vocalization detection, and automated behavioral analysis

We welcome applications from curious and highly motivated PhD candidates and postdoctoral researchers who are interested in these questions and in working across disciplines. Applicants may come from neuroscience, biology, engineering, computer science, physics, or related fields.

For questions, informal inquiries, and applications, please contact Prof. Richard Hahnloser. To apply, please include your CV, motivation letter, and MSc transcript (for PhD applicants).