Postdoctoral Associate - Cancer biology and bioinformatics

Job Duties

• 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.
• Conducts the multi-omics data analysis including RNA-seq, proteomics, metabolomics, ChIP-seq, single-cell RNA-seq, and spatial data and multi-omics data integration.
• Executes molecular and biochemical assays such as qPCR, Western blotting, ELISA, flow cytometry, and functional phenotyping.
• Contributes to the generation and characterization of knockout (KO) mouse models, including colony maintenance, genotyping, and phenotypic analysis.
• Performs mouse studies using immune-competent strains and immune-compromised NSG mice that includes tumor implantation, drug treatment, and immune profiling.
• Establishes and monitors orthotopic xenograft models to study tumor growth and progression within relevant tissue microenvironments.
• Conducts luciferase labeling of cancer cells and use bioluminescence imaging to trace tumor growth and metastasis in vivo.
• Performs mechanistic studies examining cancer signaling pathways, cytokine biology, therapeutic resistance, and tumor metabolism.
• Designs and executes xenograft, orthotopic, syngeneic, and PDX in vivo studies.
• Develops and maintains 3D organoids/spheroids and support in vivo therapeutic testing.
• Collaborates with experimental biologists, computational biologists, and clinicians to drive mechanistic and translational outputs.
• Prepares manuscripts, figures, and grant-related materials, and organize data for grant/paper submissions.
• Presents research findings at internal meetings and national/international conferences.
• Mentor’s students and support collaborative projects across laboratories.

Minimum Qualifications

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

Preferred Qualifications

• Strong coding experience using R and Python for data analysis.
• Expertise in the biological interpretation of multi-omics datasets (RNA-seq, proteomics, metabolomics, ChIP-seq, single-cell RNA-seq, and spatial data) and experience in multi-omics data integration.
• Ability to develop algorithms and code for innovative multi-omics data analysis.