Postdoctoral Fellow – Bioinformatics, Cancer Biology, Predictive Biomarkers

City of Hope

Postdoctoral Fellow in Bioinformatics conducting analysis of multi-omic datasets to inform cancer treatment strategies. Collaborating with teams to build scalable analysis pipelines.

Posted 7/30/2026full-timeDuarte • California • 🇺🇸 United StatesMid-LevelSenior💰 $34 - $38 per hourWebsite

Core Competencies

Role fit

Core Competencies

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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.

Highest-signal resume keywords

PhD In BioinformaticsCancer Biology And Genomics KnowledgeProficiency In R/Bioconductor And PythonExperience With Liquid Biopsy TechniquesStrong Applied Statistics For Genomic Data

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Somatic Variation AnalysisReproducible Pipeline DevelopmentLarge-Scale Sequencing Data ProcessingStandard Cancer-Genomics AnalysesStatistical Analysis For Genomic DataMultimodal Data IntegrationMachine Learning For ClassificationLongitudinal Study DesignBioinformatics Workflow DevelopmentTumor Microenvironment Analysis

Soft Skills

Clear Communication SkillsMentoring Trainees

Tools & Technologies

AWSGCPAzureDockerSingularityBWASTARMutect2SeuratScanpy

Industry Keywords

Cancer BiologyGenomicsLiquid BiopsyBiomarker ValidationTumor StatesMicroenvironmentDrug-Response BiomarkersSingle-Cell RNA-SeqCtDNACfDNA

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformPython

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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 toolchains (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.

Benefits

Comp & perks

Comprehensive Benefits
Health insurance
Pension scheme