We are always looking for talented, motivated, team-oriented people.
Undergraduates and graduate students are welcomed, please email me.
Post-Doc in Rodent Neurophysiology and Behavior
This position has the specific focus of recording from multiple cortical areas during a novel 3D rat reaching task we have recently developed (see R&D section). Our goal is to understand the functional organization and dynamic coordination of distributed cortical networks producing complex behaviors. Development and application of methods to understand network-level phenomena will be performed in collaboration with other lab members. Utilization of optogenetics for manipulation of specific neural circuits underlying behaviors is a goal, as is testing novel multi-thousand channel neural recordings systems. To apply, please send CV to:[email protected]
Post-Doc in Rodent Electro- and Optophysiology
We have an opening for a joint postdoc between the Feldman lab and Kris Bouchard’s lab at UC Berkeley/LBNL. The goal is to determine the cellular and laminar origins of micro-electrocorticography (ECoG) signals in rodents, using ECoG recording, laminar polytrode spike recordings, and optogenetics in genetically identified cell types. The position is fully funded for 4 years via a new NIH R01 grant. ECoG is an exciting technology for monitoring columnar- and map-scale cortical activity, with translational relevance to humans, and with applications for studying mesoscopic population codes in learning and in disease. This project will involve both hands-on electrophysiology and optogenetics, and quantitative data analysis. Applicants should have a PhD, a background in either neuroscience or neural engineering, and in vivo recording experience in rodents. Experience with Python, Matlab or other scientific programming languages is required. We offer a collaborative, multi-disciplinary environment with scientific and professional skills training.
Post-Doc in Deep Learning for Time Series Data
The Bouchard lab UC Berkeley/LBNL and the Yu group at UC Berkeley are looking for a joint post-doctoral researcher to work at the interface of data science, deep learning, and neural time-series data. The goal is to develop methods to better understand distributed brain dynamics. This position will focus on developing and applying principles of stability to recurrent neural networks towards learning scientifically meaningful representations. This position will include a strong collaboration with both basic and clinical neuroscience labs across UCSF, UC Berkeley, and University of Washington (UW). This collaboration is part of the broader Weill Neurohub (spanning UCB, UCSF, & UW) which aims to advance basic neuroscience translation to addressing neurological and psychological disorders. To apply, please send CV to:[email protected]