Over the last decades optical methods and machine learning have been revolutionizing our understanding of the brain. The ability to interact with the nervous system (both read-out and modulation) by using photons has enabled previously inaccessible experiments, like simultaneously recording and modulating the activity of genetically targeted neuronal populations at single cell resolution for weeks or months. Supervised and unsupervised algorithms have unlocked the ability to process in real-time a huge stream of information, thereby enabling closed-loop experiments where the activity of the brain is perturbed based on the network and behavioral states.

Applications are invited for a post-doctoral research position in the laboratory of Dr. Andrea Giovannucci (NEL LAB, https://nel.bme.unc.edu), within the UNC/NCSU department of Bioengineering, to develop machine learning algorithms capable of operating in closed-loop with living brain tissue. This project involves close collaborations with experimentalists, experts in optics (UNC) and machine learning scientists (Flatiron Institute).

The primary focus of the lab is to study the mechanisms and principles of neural circuit interaction between neocortex and subcortical areas, with the final objective of developing neuroprosthetic devices. With this goal, computational and closed-loop computational methods to perform simultaneous brain recording and stimulation in different brain areas will be designed, with emphasis on all-optical tools (voltage and calcium imaging, optogenetics). Both supervised (deep networks) and unsupervised (clustering and online optimization) approaches will be applied to the inference of brain states and optimal stimulation parameters from thousands of simultaneously recorded neurons. Collaborations with other laboratories within UNC and NCSU, as well as Duke University and the Simons Foundation are possible and strongly encouraged.

The ideal candidate will have a PhD in physics, computer science, computational neuroscience, machine learning, engineering or related field. She/he will have extensive experience in developing algorithms in Python, a good grasp of modern machine learning concepts, and the desire to strongly interact with experimentalists and large brain imaging datasets. Active participation in open source projects is a plus.

The department has joint affiliations with both UNC and NCSU, two of the three major universities in the research triangle, an area encompassing Chapel Hill, Durham and Raleigh. The research triangle offers a wide variety of academic, industrial and artistic opportunities, as well as affordable housing options for both families and young adults. The intellectual and artistic environment is vibrant and dynamic. The RDU international airport, just 20 minutes from the campuses, serves most American cities, as well as London and Paris with direct flights.

Salary is based on research experience. Applicants should send a brief statement of research interests, a CV and the names of two references to Prof. Andrea Giovannucci at andrea.giovannucci@gmail.com. The NEL lab supports diversity and encourages the applications from female and minority candidates.

References

[1] Giovannucci, Andrea, et al. “Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning.” Nature neuroscience 20.5 (2017): 727.

[2] Giovannucci, Andrea, et al. “Onacid: Online analysis of calcium imaging data in real time.” Advances in Neural Information Processing Systems. 2017.

[3] Giovannucci, Andrea, et al. “Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching.” IEEE Big Data 2018. (2018). In Press.

[4] Giovannucci, A., et al. “Automated gesture tracking in head-fixed mice.” Journal of neuroscience methods 300 (2018): 184-195.

[5] Giovannucci, Andrea, et al. “CaImAn: An open source tool for scalable Calcium Imaging data Analysis.” bioRxiv (2018): 339564.

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