Postdoctoral Associate - Specialist - Cancer Genomics

Summary

The Postdoctoral Associate - Specialist will analyze and interpret large-scale leukemia genomic and multiomic datasets. The role focuses on WGS, WTS, DNA methylation, long-read sequencing, ATAC-seq, HiChIP, and single-cell RNA/ATAC data, with emphasis on variant interpretation, subtype classification, model validation, data harmonization, and reproducible outputs.

The Postdoctoral Associate will train or mentor students, or junior analysts as the laboratory grows. Study complexity includes high-dimensional genomic and epigenomic datasets from pediatric leukemia cohorts, external validation cohorts, and Texas Children's cohorts. The role requires integration of sequencing data, clinical covariates, model outputs, and biological interpretation with reproducible documentation.

Baylor College of Medicine typically follows similar to the NIH stipulated stipend guidelines for Postdoctoral Associates.

Job Duties

• Analyzes and interprets genomic, transcriptomic, epigenomic, long-read, chromatin-accessibility, chromatin-contact, and single-cell datasets from pediatric leukemia studies.
• Performs variant interpretation, structural variant annotation, subtype classification, fusion/gene-expression analysis, methylation analysis, and multiomic integration.
• Supports validation/evaluation of multiomic classifiers and risk models, including benchmarking, performance assessment, and documentation of research-use outputs.
• Participates in testing, documentation, QC review, and implementation of reproducible WGS/WTS and multiomic workflows with the PI and institutional bioinformatics teams.
• Prepares data summaries, figures, reports, manuscripts, and presentations for collaborative research projects.
• Coordinates long-read sequencing and DNA methylation analysis to identify cryptic regulatory and structural variants in pediatric leukemia/lymphoma, that includes breakpoint interpretation, allelic phasing, methylation-aware analysis, and integration with transcriptomic evidence.
• Manages multiomic classifier and risk-model validation that includes evaluation against existing diagnostic workflows, turnaround time, and incremental biomarker yield.
• Manages regulatory-state and relapse-biology analyses integrating scRNAseq, scATAC-seq, bulk ATAC-seq, enhancer-promoter connectivity data, reference atlases, and diagnosis-relapse pairs.
• Coordinates QC review, documentation, and implementation support for WGS/WTS workflows.
• Performs other job-related duties as assigned.

Minimum Qualifications

• Ph.D. in Chemistry, Computational Sciences, Computational Biology, Structural Biology, Computer Science, Bioinformatics, Statistics, or related disciplines. May also include Ph.D. in Biology or Biomedical Sciences in combination with an M.S. or extensive multidisciplinary experience in one of the above quantitative fields.

Preferred Qualifications

• PhD in computational biology, bioinformatics, genomics, statistics, computer science, biomedical sciences with strong quantitative training, or related field.
• Experience in WGS, WTS/RNA-seq, DNA methylation, long-read sequencing, ATAC-seq, HiChIP/chromatin-contact data, scRNA-seq, scATAC-seq, cancer genomics, structural variation, R, Linux/HPC, version control, and reproducible workflows.