Postdoctoral Fellow – Bioinformatics, Cancer Biology, Predictive Biomarkers

• Build reproducible pipelines for serial tumor and liquid-biopsy data across large cohorts.
• Analyze somatic variation (SNV/indel, CNV/SV), methylation, and deconvolution in longitudinal ctDNA and tissue.
• Integrate bulk and single-cell RNA-seq with genomic/epigenomic data to define tumor states, microenvironment, and resistance programs.
• Co-develop and validate drug-response biomarkers with computational, experimental, and clinical teams.
• Publish and present results, mentor trainees, and grow an independent research direction.

• PhD (or equivalent) in bioinformatics, computational biology, genomics, systems biology, biomedical engineering, statistics, computer science, or a related quantitative field (completed within five years or expected within six months).
• Demonstrated expertise across most of the following areas:
• Cancer biology and genomics domain knowledge
• Working understanding of cancer biology and signaling, and how DNA mutation/methylation affects RNA and protein.
• Familiarity with standard cancer-genomics analyses (somatic calling, CNV/SV, mutational signatures, purity/ploidy, clonal structure).
• Liquid biopsy experience (ctDNA/cfDNA methylation/CTCs) and knowledge of biomarker validation frameworks preferred.
• Proven ability to process large-scale sequencing data end-to-end and build reproducible workflows on HPC and/or cloud (AWS/GCP/Azure).
• Proficiency with R/Bioconductor and Python, git, containers (Docker/Singularity), and core genomics formats (BAM/CRAM, VCF, MAF).
• Familiarity with common aligners/callers and single-cell tool chains (e.g., BWA/STAR, Mutect2, Seurat/Scanpy) is expected.
• Strong applied statistics for genomic data (multiple testing, multivariate methods, dimensionality reduction, differential expression/methylation, survival analysis).
• Experience with longitudinal designs, batch correction, and multimodal integration is valued; ML for classification/response prediction is a plus.
• First-author publications (or preprints) and clear communication skills to work with wet-lab biologists, clinicians, and computational scientists.

Demonstrates expertise in bioinformatics and computational biology, with a strong focus on cancer genomics, data analysis, and reproducible workflow development. Proficient in processing large-scale sequencing data and integrating multi-modal datasets to derive insights into tumor biology and drug response.