The project seeks to explore mechanisms underlying
dynamical self-reconfiguration of neuronal networks in brains and machines. Areas of interest are broad and include adaptive processing in dynamically self-reconfiguring neuronal networks,
coordination of large scale distributed
computation as well as energy efficient neuromorphic hardware. A potential research direction is to explore oscillatory mechanisms to coordinate computation and implement those into neuromorphic hardware of
super-conducting oscillators.
The ideal candidate would be capable of creating novel ideas and frameworks for flexible analog computation in neuronal and artificial networks. Experience with computation in dynamical systems, theoretical and experimental neuroscience, machine learning,
neuromorphic computing or hardware design are advantageous.
Interactions between researchers at UC San Francisco, UC Berkeley and at LBNL are encouraged. Postdoctoral researchers will have access to allocations on some of the world-leading supercomputers including Cori, and Perlmutter (Berkeley/NERSC).
The lab is committed to providing a diverse and inclusive environment for all scholars, and applications are especially encouraged from all underrepresented groups. Additionally, the lab is committed to the professional development of the members, making it
valuable preparation for people who are interested in academic, industrial, or entrepreneurial careers. The position has no mandatory teaching or administrative duties. Excellent (written and oral) communication skills in English are required
Candidates should send a CV, a statement of research experience and interests, expected date of availability, and the contact information for three references to
christoph.kirst@ucsf.edu. Application review will proceed until the position is filled.