Postdoctoral Fellow-MSH-30025-250

About the role

The Izzo Lab (Laboratory of Clonal Evolution and Molecular Epigenetics of Cancer) at the Icahn School of Medicine at Mount Sinai is seeking a highly motivated Postdoctoral Fellow in Bioinformatics to join our team. Our research focuses on uncovering the fundamental mechanisms driving the onset, therapy resistance, and progression of hematological malignancies, with a particular emphasis on clonal evolution, genetic heterogeneity, and epigenetic plasticity. We develop and apply cutting-edge single-cell sequencing technologies (including multi-omic approaches integrating genomics, transcriptomics, and epigenomics) to directly profile patient samples. This work aims to map genotype-to-phenotype relationships, understand how somatic mutations drive epigenetic deregulation, and identify vulnerabilities that can be targeted to overcome therapy resistance and improve patient outcomes.

Responsibilities

• Perform advanced bioinformatics analysis of single-cell multi-omic data (scRNA-seq, scATAC-seq, multiome, CITE-seq, and emerging spatial/single-cell technologies), including quality control, integration, clustering, trajectory inference, and differential analysis.
• Develop and apply custom computational methods or pipelines to study genetic heterogeneity, clonal evolution, somatic mutation effects, and epigenetic regulation (e.g., chromatin accessibility, gene regulatory networks, and plasticity).
• Integrate multi-modal datasets with clinical and functional data to generate biological insights and hypotheses.
• Collaborate closely with wet-lab team members on experimental design, data interpretation, and validation.
• Contribute to manuscript preparation, grant writing, and presentation of findings at conferences.
• Mentor junior lab members and participate in lab meetings and journal clubs.

Qualifications

• Ph.D. (or M.D./Ph.D.) in Bioinformatics, Computational Biology, Genomics, Biostatistics, or a closely related field.
• Strong expertise in single-cell sequencing analysis, with demonstrated experience in R/Bioconductor and/or Python (e.g., Seurat, Scanpy, ArchR, Signac, or similar tools).
• Proficiency in handling large-scale genomic datasets, statistical modeling, machine learning (optional but a plus), and high-performance computing (e.g., SLURM clusters).
• Familiarity with cancer biology, epigenetics, or clonal evolution is highly desirable.
• Excellent communication skills, ability to work independently and in teams, and a track record of peer-reviewed publications.
• Prior experience with hematological malignancies or multi-omic integration is a strong advantage.