The Data Science Institute (DSI) and the Department of Psychology at Vanderbilt University invites applications for its first cohort of applied deep learning postdoctoral fellows. Fellows will be part of an interdisciplinary Deep Learning Initiative to explore the application of transformers (e.g., the same class of models as ChatGPT) and other AI models to current state-of-the-art datasets in Neuroscience. Fellows will serve as collaborators on research projects as part of a team of researchers, data scientists, graduate students studying data science, and undergraduates.
The Data Science Institute (DSI) and the Department of Psychology at Vanderbilt University invites applications for its first cohort of applied deep learning postdoctoral fellows. Fellows will be part of an interdisciplinary Deep Learning Initiative to explore the application of transformers (e.g., the same class of models as ChatGPT) and other AI models to current state-of-the-art datasets in Neuroscience. Fellows will serve as collaborators on research projects as part of a team of researchers, data scientists, graduate students studying data science, and undergraduates. The Fellow would have a primary appointment in the Bastos Lab (Department of Psychology) with a secondary appointment in the Data Science Institute. Fellows will have access to compute infrastructure including two DGX A100s for model fitting and inference. We have recently partnered with the Allen Institute through the OpenScope program. Through this partnership we have exclusive access to multi-area high-density data in mice in a predictive coding task (~20,000 neurons recorded with Neuropixels). We are simultaneously recording comparable neuronal data in non-human primates at Vanderbilt. This allows us to study primate-specific areas and more complex behaviors. Using optogenetics, we have access to specific inhibitory neurons (Parvalbumin or Somatostatin positive in mice, GABA-ergic in primates). Our ultimate aim is to use deep learning techniques on these datasets to discover the neural implementation of predictive coding. This would enable us to discover whether prediction is a fundamental neuronal computation as previously proposed.
Strong candidates will have a desire to develop greater expertise in the application of deep learning to high-density multi-species neural spiking and local field potential data. We invite applications from PhD’s from a broad range of fields (for example neuroscience, psychology, computer science, engineering, applied mathematics, medicine, and others). The essential requirements are a strong desire to make discoveries about predictive coding in the brain, a good working knowledge of some area of deep learning, and a strong interest in learning and building expertise in applying deep learning.
Fellows will play an important part in the Deep Learning Initiative and will be part of a strong team of data scientists, experimentalists and data science graduate students. In addition to their home position in the Bastos lab, this Postdoc will be part of a community at the DSI which includes other postdocs where we share research, collaborate on problem solving, mentor students, and support reproducible, open research.
The cohort of fellows will also have the opportunity to develop greater expertise by taking classes and workshops. Fellows will also have the opportunity to contribute to the educational mission of the DSI by assisting in the creation and instruction of an advanced research-based graduate course involving deep learning if they so wish.
Fellowship Details Fellowships will begin in Spring of 2023 and will be renewable for up to three years. Fellows will receive annual salary support of $70,000, a competitive benefits package, and an annual research budget of $10,000 that can be used for travel, equipment, software, or other research expenses.
Eligibility Candidates should have a PhD or be on track to earn a PhD before they begin their tenure as DSI fellows. Successful candidates will have a strong record in computational neuroscience, experimental neuroscience, or deep learning related research. Candidates should have an interest in engaging in interdisciplinary work. Previous experience with recording/analyzing multi-channel neurophysiological data and experience with automated spike-sorting tools (e.g., Kilosort) is preferred, but not required.
Application Procedure To apply, please submit the following materials to: email@example.com firstname.lastname@example.org ● A brief cover letter that states the overall goals and motivation for applying (1 page) ● A curriculum vitae ● A publication list ● A single document containing a brief statement of past research accomplishments and future research interests (2 pages)
In addition, please arrange for three letters of reference to be submitted. Vanderbilt University is committed to principles of equal opportunity and affirmative action. Candidates that will diversify the workforce in data science are especially encouraged to apply.