Job DescriptionWe 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 or related field; PhD in Computational Biology, Bioinformatics, Genomics, Statistics, Computer Science, or related field highly preferred
- 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
