Senior Scientist (Computational Genomics & Large-Scale Molecular Data Analysis)(Dr Sealfon's Lab) - Neurology
About The Position
The Senior Scientist in Dr. Sealfon’s Lab in Neurology will drive computational analysis of high-dimensional datasets, working closely with a computational-experimental team to generate biological insights from complex genomic data. The role focuses on single-cell transcriptomics and epigenomics, large-scale datasets, and quantitative and programming methods. Machine learning and AI approaches are highly desirable. The position leads analysis of single-cell RNA-seq and multiome datasets, integrates data across modalities, and develops processing pipelines for novel single-cell multiomic technologies. The Senior Scientist also applies statistical modeling and machine learning to characterize cellular states, regulatory programs, and epigenetic signatures; designs integrative multi-omic studies across cohorts and experimental systems; presents findings; and contributes to publications and grant applications. Other related duties may be performed.
Requirements
- Ph.D. in sciences or a related field, such as Computational Biology, Bioinformatics, Genomics, Statistics, or Computer Science.
- Minimum five years of experience with scientific investigation.
- Demonstrated outstanding achievement in a field of research.
- Contributions to peer-reviewed publications, inventions, or similar work, at least some of which had a major impact on advancing the field or discipline.
Nice To Haves
- Prior experience successfully securing external grant funding is highly desirable.
- Strong experience analyzing bulk and single-cell RNA-seq and epigenomic data.
- Proficiency in R, including common single-cell analysis frameworks.
- Experience working with large-scale genomic datasets and high-performance computing environments.
- Strong statistical background and data visualization skills.
- Experience analyzing DNA methylation data using array-based or sequencing-based approaches.
- Experience analyzing long-read RNA sequencing datasets.
- Demonstrated use of machine learning or AI methods for genomic data integration or prediction.
- Familiarity with cloud-based workflows and reproducible pipeline development.
- Track record of publications in peer-reviewed journals.
Responsibilities
- Lead analysis of single-cell RNA-seq and multiome datasets, including joint RNA/ATAC profiling.
- Perform integrative analysis across modalities, including bulk RNA-seq, ATAC-seq, and DNA methylation datasets.
- Develop processing pipelines for novel single-cell multiomic technologies.
- Apply statistical modeling and machine learning methods to identify cellular states, regulatory programs, and epigenetic signatures.
- Design and implement integrative multi-omic analyses across cohorts and experimental systems.
- Present findings internally and contribute to publications and grant applications.
- Stay current with emerging single-cell and AI-driven genomic analysis methodologies.
- Perform other related duties.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
