Postdoc, Research Scientist and Postbac positions in computational single-cell RNA sequencing and spatial transcriptomics & proteomics are available in the laboratory of Dr. Yuval Kluger at Yale University School of Medicine. The Kluger Lab is affiliated with the Applied Mathematics program in the Department of Mathematics. Students in the lab pursue PhD degrees in Computational Biology & Bioinformatics, Applied Mathematics and Statistics and Data Science.
The group is focused on methodological development of novel computational methods including deep learning, graph theory, and spectral approaches to analyze high-throughput and high-dimensional biomedical data. Most of our current applications concentrate on analysis of data generated in single cell RNA sequencing and spatial transcriptomics/proteomics studies which provide unprecedented opportunities to conduct detailed analyses of cell subpopulations. Fulfilling the promise of these studies and biomarker discovery requires robust computational approaches to support detection of rare phenotypes and unanticipated cellular responses. Current approaches for denoising, calibration, clustering and visualizing of such data types suffer from challenges such as erroneous imputation of non-expressed genes, removal of multivariate batch effects, detection of local differences between cell distributions, and inefficiencies of clustering and dimensional reduction methods of very large datasets. We have developed novel and efficient spectral and neural network prototypes suitable for addressing these issues in high throughput data contexts and further develop and adapt these methods to data generated by our collaborators.
The group is actively engaged in multi-disciplinary national collaborations with large project teams as well as multiple groups from the Departments of Immunobiology, Neurology, Genetics, Internal Medicine, Pathology, as well as the Cancer Center. The successful candidate will utilize single cell transcriptomics, spatial transcriptomics as well as other genomic, epigenomic and proteomic data from our experimental collaborators and from the public domain and develop or implement novel techniques to characterize a variety of biological systems. The successful candidates must be able to work well with groups from different disciplines.
Research projects may involve, but not limited to:
- Analysis of high throughput transcriptomics, proteomics, genomics data, such as single cell RNA sequencing data, spatial transcriptomics data in a collaborative setting.
- Data management and pipeline development for consortiums.
- Develop or implement new bioinformatics and computational biology algorithms
- Develop or implement Deep Learning and computational approaches to tabulated or hyperspectral data
- Ph.D. or undergraduate degree in Computational Biology, Bioinformatics, Genetics, Computer Science, Applied Mathematics, Statistics, Physics, Engineering or other related fields.
- Proficiency with the analysis of next generation sequencing data is a plus.
- Ability to implement and benchmark computational methods.
- Ability to develop computational methods is a plus.
- Solid programming skills.
- Experience with Python or R programming languages is preferred.
- Interest in collaborating with biologists and physician scientists.
- Familiarity with UNIX and cluster computing is preferred.
Qualified candidates who are interested in applying should e-mail an application package, including: statement of interest, CV, and contact information for references. Please send your application package to Dr. Yuval Kluger (yuval.kluger@yale.edu). In the subject line, write “your full name, postdoc, postbac and research scientist application”.