Summary

Dr. Putluri’s laboratory at Baylor College of Medicine is seeking a highly motivated and talented Postdoctoral Fellow in Cancer Bioinformatics, Cancer biology, and Biostatistics to join our multidisciplinary cancer research team.
The Postdoctoral Associate will lead computational and statistical analyses of high-dimensional multi-omics and spatial datasets generated from cancer models and translational human specimens. The position will focus on developing and applying innovative computational approaches to understand molecular mechanisms of cancer progression, metabolic reprogramming, therapeutic resistance, and tumor–immune interactions.
The Postdoctoral Associate will work with diverse datasets that includes bulk RNA-seq, single-cell RNA-seq, proteomics, metabolomics, ATAC-seq, ChIP-seq, spatial transcriptomics, spatial proteomics, spatial metabolomics and imaging mass cytometry (CyTOF). A major emphasis will be placed on multi-omics data integration, statistical modeling, biomarker discovery, pathway/network analysis, and development of reproducible computational workflows.
This position provides an excellent opportunity to work at the interface of cancer biology, computational biology, bioinformatics, and biostatistics, with access to state-of-the-art multi-omics technologies, patient-derived models, 3D cancer models, and clinically annotated human specimens.
The Postdoctoral Associate will work on cutting-edge translational cancer research projects involving tumor cell signaling, metabolic reprogramming, immuno-oncology, and therapeutic target identification. The lab focuses on understanding metabolic and molecular vulnerabilities driving cancer progression, therapeutic resistance, and tumor–immune interactions. This position offers opportunities to work with cutting-edge cancer models, 3D culture systems, and translational human samples. This position offers the opportunity to work with state-of-the-art platforms that includes patient-derived models (PDXs), 3D organoids/spheroids, advanced molecular assays, and multi-omics datasets.

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

Job Duties

• Develops, implements, and maintains reproducible bioinformatics pipelines for large-scale cancer genomics, transcriptomics, proteomics, metabolomics, epigenomics, and spatial datasets.
• Analyzes bulk RNA-seq data, including quality control, normalization, differential expression, pathway enrichment, gene-set enrichment analysis (GSEA), and molecular signature development.
• Analyzes single-cell RNA-seq datasets that includes quality control, dimensionality reduction, clustering, cell-type annotation, differential expression, cell-state analysis, trajectory analysis, and cell–cell communication.
• Performs computational analysis of spatial transcriptomics data, including spatially variable features, spatial clustering, cell-type deconvolution, spatial interactions, and integration with single-cell datasets.
• Analyzes proteomics and metabolomics datasets, including data preprocessing, normalization, statistical testing, differential abundance analysis, pathway enrichment, network analysis, and integration with transcriptomic datasets.
• Analyzes ATAC-seq and ChIP-seq data, including quality control, peak identification, motif analysis, transcription-factor activity, chromatin accessibility, and integration with gene-expression data.
• Analyzes imaging mass cytometry/CyTOF datasets, including high-dimensional cell phenotyping, clustering, spatial organization, cell–cell interaction analysis, and statistical modeling.
• Develops computational approaches for multi-omics integration to identify molecular pathways, regulatory networks, metabolic vulnerabilities, biomarkers, and therapeutic targets.
• Develops and applies machine learning and statistical modeling approaches for biomarker discovery, classification, prediction, and patient stratification.
• Develops algorithms and computational methods to address innovative questions in single-cell, spatial, metabolic, and multi-omics biology.
• Establishes standardized workflows for data quality control, reproducibility, version control, and computational documentation.
• Performs large-scale data processing using high-performance computing (HPC) and cloud-based computational resources, when appropriate.
• Performs multivariable regression, survival analysis, longitudinal analysis, correlation analysis, clustering, dimensionality reduction, and predictive modeling.
• Conducts in vitro cancer biology experiments, including 2D culture, 3D organoids, spheroids, and patient-derived models.
• Performs CRISPR/Cas9-mediated gene knockout (KO), siRNA/shRNA-mediated knockdown (KD), and validation of engineered cell lines.
• Executes molecular and biochemical assays such as qPCR, Western blotting, ELISA, flow cytometry, and functional phenotyping.
• Performs mouse studies using immune-competent strains and immune-compromised NSG mice, including tumor implantation, drug treatment, and immune profiling.
• Conducts luciferase labeling of cancer cells and use bioluminescence imaging to trace tumor growth and metastasis in vivo.
• Prepares manuscripts, figures, and grant-related materials, and organize data for grant/paper submissions.
• Presents research findings at internal meetings and national/international conferences.
• Performs other job-related duties as assigned.

Minimum Qualifications

• MD or Ph.D. in Basic Science, Health Science, or a related field.
• No experience required.

Preferred Qualifications

• Ph.D. in Bioinformatics, Computational Biology, Biostatistics, Statistics, Biomedical Informatics, Data Science, cancer biology, Genomics, Systems Biology, or a related quantitative field.
• Strong programming experience in R and/or Python.
• Demonstrated experience analyzing one or more high-throughput datasets, such as RNA-seq, single-cell RNA-seq, spatial transcriptomics, proteomics, metabolomics, ATAC-seq, ChIP-seq, or CyTOF/imaging mass cytometry.
• Experience with statistical analysis of large biological datasets and strong understanding of experimental design and statistical methodology.
• Experience with Linux/Unix environments, command-line tools, and computational workflows.
• Experience with data visualization using R, Python, or related computational platforms.
• Ability to independently develop, troubleshoot, and document computational workflows.
• Strong ability to handle large datasets in Microsoft Excel and analyze data using GraphPad Prism, including statistics and data presentation.
• Experience working with 3D culture systems, including organoids or spheroids.
• Experience working with mouse models (xenograft, orthotopic, or PDX studies).
• Proficiency in R or Python for biological data analysis will plus.
• Strong publication record and excellent communication skills.
• Ability to work independently and collaboratively in a fast-paced research environment.