DescriptionWe are seeking a highly motivated Staff 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 Staff 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
Qualifications- Ph.D. in Biological Science or related field
- Three years experience
Preferred Skills
- PhD in Computational Biology Bioinformatics Genomics Statistics Computer Science or related field (or equivalent 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
Required Experience:
IC
DescriptionWe are seeking a highly motivated Staff 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 Staff...
DescriptionWe are seeking a highly motivated Staff 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 Staff 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
Qualifications- Ph.D. in Biological Science or related field
- Three years experience
Preferred Skills
- PhD in Computational Biology Bioinformatics Genomics Statistics Computer Science or related field (or equivalent 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
Required Experience:
IC
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