Associate Scientist - Computational Genomics & Large-Scale Molecular Data Analysis - (Dr Sealfon's Lab) - Neurology

We are seeking a highly motivated Associate Scientist to lead and support the analysis of large-scale molecular datasets in a dynamic, collaborative research environment. This role focuses on cutting-edge functional genomics, with an emphasis on single-cell and multi-omic technologies. The Associate Scientist will drive computational analysis of high-dimensional datasets, partnering closely with a well-integrated computational-experimental team to generate biological insights from complex genomic data.

The ideal candidate has deep expertise in single-cell transcriptomics and epigenomics, experience handling large-scale datasets, and strong quantitative and programming skills. Experience in machine learning and AI approaches is highly desirable. Responsibilities Lead analysis of single-cell RNA-seq and multiome datasets (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 Performs other related duties.

Qualifications Ph. D. in Biological Science, Computational Biology, Bioinformatics, Genomics, Statistics, Computer Science, or related field Three years’ experience 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 (e.g., array-based or sequencing-based approaches) Experience analyzing long-read RNA sequencing datasets Demonstrated use of machine learning/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

Full time · Day shift