Applications are invited for a joint post-doctoral research position in the laboratories of Computational Biophotonics (N. Pegard, UNC, http://www.nicolaspegard.com/) and Neural Engineering (A. Giovannucci, UNC/NCSU, https://nel.bme.unc.edu), to develop optical, computational and neurophysiological methods to simultaneously modulate and monitor neuronal activity at single-cell resolution in the brain of behaving rodents. The collaboration will develop new approaches for functional imaging and optical modulation of the brain at different locations concurrently, thus opening the path to the study of the interactions among distant areas within the central nervous system.
Over the last decades, optical methods and machine learning have revolutionized our understanding of the brain. The ability to optically interact with the nervous system, using photons to monitor and perturb activity at the scale of individual neurons, has enabled previously inaccessible experiments, such as simultaneous recording and modulation of the activity in genetically targeted neuronal populations at single cell resolution for months [1-4]. Supervised and unsupervised algorithms have unlocked the ability to process huge streams of information in real time [5-9], thereby paving the way for closed-loop experiments where the activity of the brain is perturbed in real time based on the network and behavioral states. The Computational Biophotonics and Neural Engineering laboratories provide a unique opportunity to develop an interdisciplinary expertise in optics, machine learning, and experimental neuroscience, with research projects that combine system design and assembly, algorithms for data acquisition and processing, and experiments in awake rodents. The candidate will be exposed to and benefit from the members of both labs, with access to experts in optics, neurophysiology and machine learning. The ideal candidate will have a background in optics, physics, or electrical engineering and a strong interest for both software and hardware development. Coding experience in Python or Matlab is a plus, and an interest for modern machine learning concepts is expected. Additional interest and prior experience in neuroscience, biophotonics, or a related area is a plus.
The laboratories have affiliations with 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 either Prof. Nicolas Pégard (pegard at unc dot edu) or Prof. Andrea Giovannucci (andrea dot giovannucci at gmail dot com). Our laboratories support diversity and encourage the applications from female and underrepresented minority candidates.
 Pégard, Nicolas C., et al. “Three-dimensional scanless holographic optogenetics with temporal focusing (3D-SHOT).” Nature Communications 8,1, 2017.
 Giovannucci, Andrea, et al. “Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning.” Nature neuroscience 20,5, 2017.
 J. Zhang, N. Pégard, et al. “3D computer-generated holography by non-convex optimization,” Optica 4, 1306-1313, 2017.
 A. R. Mardinly, I. A. Oldenburg, N. C. Pégard, et al. “Precise multimodal optical control of neural ensemble activity” Nature Neuroscience, 21, 881–893, 2018.
 Giovannucci, Andrea, et al. “Onacid: Online analysis of calcium imaging data in real time.” Advances in Neural Information Processing Systems. 2017.
 Giovannucci, Andrea, et al. “CaImAn: An open source tool for scalable Calcium Imaging data Analysis.” bioRxiv (2018): 339564.
 Giovannucci, Andrea, et al. “Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching.” IEEE Big Data 2018. 2018. In Press.
 Pégard, Nicolas, et al. “Compressive light-field microscopy for 3D neural activity recording,” Optica 3, 517-524, 2016.
 Giovannucci, A., et al. “Automated gesture tracking in head-fixed mice”. Journal of neuroscience method. Journal of neuroscience methods. 300, 184-195 (2018)