Job Vacancy Announcement – Title-42 Staff Scientist
Department of Health and Human Services (HHS)
National Institutes of Health (NIH)
National Institute of Mental Health (NIMH)
Intramural Research Program (IRP)
Laboratory of Molecular Biology
Section on Neural Function (SNF)
NIMH is seeking exceptional candidates for a Staff Scientist position in the Division of Intramural Research Programs (IRP). The appointment will be in the Section on Neural Function (SNF) in the Laboratory of Molecular Biology, but the position is shared between the SNF and the Systems Neuroscience Imaging Resource (SNIR). Both the SNF and SNIR conduct state-of-the-art imaging using a variety of commercial and custom-built advanced microscopy systems that typically generate large datasets.
A principal focus of the SNF is whole brain imaging of neural activity from the fruit fly brain using 1- and 2-photon light-sheet microscopy. The primary goal is to understand how brains compute behavioral output and use neuromodulators to initiate and stabilize changes in behavioral state. By studying a specific multiphasic behavioral sequence in the fruit fly, Drosophila melanogaster, in which neuromodulators play a key role in regulating phase transitions, the SNF aims to understand how changes in activity at both the neuronal and circuit levels are implemented. The focus of the SNIR is to broadly facilitate research within NIMH by providing access to technologies such as high-throughput wide-field microscopy, deep tissue imaging via laser scanning confocal microscopy, and light-sheet microscopy. A particular focus of the SNIR is to facilitate advances in 3D reconstruction of specified circuits, cell types, and protein distributions, particularly in large samples of rodent and primate brain tissue, combining modern clearing, image acquisition, and volume reconstruction methods.
The imaging needs of the SNF and SNIR include custom pipelines for image segmentation, registration, denoising, deconvolution and other image processing techniques as well as custom analysis and modeling software that makes use of such tools as dimensionality reduction, generalized linear models, clustering, regression, and other advanced statistical methods, including the application of these techniques to datasets that are hundreds of terabytes in size.
Candidates for this position must have a Ph.D. in data or computer science or a related discipline and at least 10 years of experience in neural imaging data analysis, including machine learning methods for image segmentation and registration. Expertise in the application of high-dimensional data analyses and strong programming skills (e.g., Matlab, Python, CUDA) are also required, as is familiarity with high-performance computing clusters and data/code sharing platforms. Familiarity with deep-learning techniques, such as convolutional neural networks and generative adversarial networks, as well as experience in software such as Tensorflow, Keras, and Pytorch, are a plus.
The Staff Scientist’s primary responsibilities will include:
- Providing programming support for ongoing development projects in the SNIR.
- Creating user-friendly software for custom applications in the SNIR.
- Developing analysis pipelines for datasets to support research in the SNF.
In addition, the individual will support and train members of both the SNF and SNIR, establish and conduct collaborative research with affiliated groups within the Institute, attend and present at national and international meetings, and assist in writing manuscripts. Salary will be commensurate with education and experience.
How to Apply
Applicants should send curriculum vitae and three letters of recommendation to Dr. Benjamin White, NIMH, PNRC, Bldg. 35-1B1012, Bethesda, MD 20892-1366, 301-435-5472, firstname.lastname@example.org.
The NIH encourages the application and nomination of qualified candidates with diverse perspectives such as underrepresented racial and ethnic groups, those with disabilities, those from disadvantaged backgrounds, and women. HHS and NIH are Equal Opportunity Employers.