Postdoctoral Fellow-MSH-30001-337

Mount Sinai Health System • New York, NY

3d

About The Position

We are seeking a highly motivated Postdoctoral Fellow to join our team and play a key role in developing, optimizing, and applying spatial and single-cell multi-omic technologies. The successful candidate will contribute to experimental method development, sequencing-based assay optimization, and associated computational analyses to study gene regulation, chromatin organization, and epigenomic mechanisms in complex biological systems. This position is ideal for a candidate with strong interests in technology development, single-cell/spatial genomics, epigenomics, and integrative analysis. The fellow will work closely with experimental and computational scientists in a highly collaborative environment and will have opportunities to lead independent projects, publish high-impact studies, and develop new tools for the broader research community. The Postdoctoral Fellow will contribute to the development of innovative spatial and single-cell multi-omic technologies and apply these approaches to address important questions in gene regulation, epigenomics, development, and disease. The position offers an excellent opportunity to gain expertise at the interface of experimental technology development, computational genomics, and biological discovery.

Requirements

• Ph.D. in Genomics, Molecular Biology, Epigenomics, Computational Biology, Bioinformatics, Biomedical Engineering, Computer Science, or a related field.
• Strong interest in developing and applying spatial and single-cell multi-omic technologies.
• Experience with single-cell or spatial genomic methods, such as scRNA-seq, scATAC-seq, CUT&Tag/CUT&RUN, DNA methylation profiling, Hi-C/chromatin conformation assays, spatial transcriptomics, or related platforms.
• Experience with next-generation sequencing library preparation, assay optimization, or molecular biology techniques is highly desirable.
• Proficiency in computational analysis using R, Python, or related programming languages.
• Familiarity with common single-cell analysis frameworks and tools, such as Seurat, Scanpy, ArchR, Signac, Cell Ranger, Snakemake, Nextflow, or related platforms, is preferred.
• Strong problem-solving skills and ability to troubleshoot both experimental and computational workflows.
• Ability to work independently while also contributing effectively to collaborative projects.
• Excellent written and verbal communication skills.
• A strong publication record or demonstrated potential for independent scientific productivity.

Nice To Haves

• Experience developing or optimizing new sequencing-based technologies.
• Experience working with primary tissues, low-input samples, embryos, organoids, tumors, or clinically derived specimens.
• Experience integrating multi-omic datasets, including transcriptomic, epigenomic, spatial, chromatin accessibility, DNA methylation, or chromatin conformation data.
• Familiarity with machine learning, statistical modeling, regulatory genomics, or gene regulatory network analysis.
• Experience with high-performance computing environments and reproducible workflow management.

Responsibilities

• Develop, optimize, and benchmark new spatial and single-cell multi-omic assays, including approaches that integrate transcriptomic, epigenomic, chromatin accessibility, DNA methylation, chromatin conformation, and/or protein-level measurements.
• Design and perform experiments to improve assay sensitivity, throughput, robustness, and compatibility with primary tissues, low-input samples, and clinically relevant specimens. Optimize sample preparation, nuclei/cell isolation, library construction, sequencing strategies, and quality-control steps.
• Generate and analyze spatial and single-cell datasets, including single-cell RNA-seq, single-cell epigenomic assays, spatial transcriptomics, and other multi-omic platforms. Perform quality control, normalization, clustering, annotation, differential analysis, trajectory analysis, and regulatory feature identification.
• Integrate transcriptomic, epigenomic, spatial, and other omics datasets to investigate gene regulatory programs, cell-state transitions, tissue organization, and disease-associated molecular mechanisms.
• Develop, customize, and maintain computational pipelines for processing, analyzing, visualizing, and interpreting large-scale single-cell and spatial multi-omic datasets. Ensure that workflows are reproducible, scalable, and well documented.
• Benchmark new methods against existing technologies using both experimental and computational metrics. Validate key biological findings through orthogonal genomic, molecular, imaging, or functional approaches.
• Work closely with lab members, collaborators, and core facilities to design experiments, analyze data, interpret results, and prepare manuscripts, grant applications, and presentations.
• Maintain clear experimental records, analysis notebooks, protocols, and code documentation. Contribute to protocol sharing, software release, manuscript preparation, and presentation of research findings at scientific meetings.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree