The Departments of Neuroscience and Biomedical Engineering at the University of Minnesota seek one or more Postdoctoral Associates for conducting work focused on understanding the role of low frequency rhythmic activity in attention and decision making.
The planned work is supported by a NIMH grant and includes both experimental and computational components. Experimentally, the work includes chronic multi-electrode recordings and stimulation in multiple brain regions of monkeys trained to perform challenging sensorimotor tasks and may expand to include ECoG recordings in humans. Computationally, the work includes issues of neural coding, including connecting real-time metrics of encoding and decoding to explain behavior, and explorations of how low frequency coherence affects both neural coding and behavioral performance. The proposed work, by combining cutting-edge experimental and computational techniques, should have broad applicability to cortical function and cognition.
These Postdoctoral Associates will be mentored by Dr. Geoffrey Ghose (Neuroscience) and will have the opportunity to collaborate with a diverse team of researchers with complementary expertise in electrophysiology, vision science, neuroimaging, and computational neuroscience.
Depending on interests and qualifications, activities may include one or more of the following:
Training non-human primates to perform sophisticated sensorimotor tasks.
Assist in recovery surgeries to implant chronic devices.
Acquire high-electrode count single-unit and multi-unit data during behavior and brain stimulation.
Develop new algorithms and models for connecting high-dimension neuronal activity to behavior.
Develop new algorithms and models for connecting synchronous rhythmic activity to neuronal encoding and decoding.
Collaboratively contribute to group efforts, and provide support and instruction where appropriate.
Design and refine experimental designs.
Make observations, interpret findings, and present results internally and externally.
Prepare conference abstracts, write journal papers, and assist in grant applications.
PhD in Computer Science, Vision Science, Engineering, Physics, Mathematics, Neuroscience or relevant field.
Proven experience in systems electrophysiology.
Track record of scientific publications (including peer-reviewed journal papers).
Track record of fellowship support.
Proficiency in Matlab and/or Python.
Internal Number: 340360
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