Postdoc, Pediatric Cancer Genomics & Multiomics

Baylor College of Medicine; Texas; On-site; USD 70,000 - 75,000; Full time

Job summary

Baylor College of Medicine in Houston, TX invites applications for a Postdoctoral Associate - Specialist in Cancer Genomics. The successful candidate will analyze and interpret large-scale leukemia genomic and multiomic data, focusing on WGS, WTS, methylation, long-read sequencing, ATAC-seq, HiChIP, and single-cell data.

Responsibilities include training or mentoring students, validating classifiers, and integrating sequencing data with clinical covariates to produce reproducible outputs.

Qualifications

• Ph.D. in a quantitative field relevant to cancer genomics.
• Experience with WGS/WTS, RNA-Seq, DNA methylation, ATAC-Seq, HiChIP preferred.
• Strong programming and data-interpretation skills.

Responsibilities

• Analyze genomic and multiomic data from pediatric leukemia studies.
• Perform variant interpretation, fusion/gene-expression analyses, and methylation analysis.
• Support validation of multiomic classifiers and risk models with rigorous documentation.
• Coordinate QC, reproducible workflows, and collaboration with bioinformatics teams.
• Prepare data summaries, figures, and manuscripts for collaborative projects.
• Lead long-read sequencing and methylation analyses with integration to transcriptomic data.
• Manage regulatory-state and relapse-biology analyses across datasets.
• Other duties as assigned.

Skills

Genomic data analysis

Multiomic integration

Pediatric cancer genomics

Education

Ph.D. in Computational Biology/Bioinformatics

Tools

R

Linux/HPC

Git

Job description

Baylor College of Medicine in Houston, TX invites applications for a Postdoctoral Associate - Specialist in Cancer Genomics. The successful candidate will analyze and interpret large-scale leukemia genomic and multiomic data, focusing on WGS, WTS, methylation, long-read sequencing, ATAC-seq, HiChIP, and single-cell data.

Responsibilities include training or mentoring students, validating classifiers, and integrating sequencing data with clinical covariates to produce reproducible outputs.