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MD Anderson Cancer Center Postdoctoral Fellow - Translational Molecular Pathology in MDA-On-Site, United States

Our group studies ecological mechanisms in early lung cancer development and response to immune-based treatment (e.g., Sinjab et al, Cancer Discovery, 2021; Hao et al, Cancer Discovery, 2022; Han et al, Nature, 2024). We seek a talented and highly motivated computational postdoctoral fellow with a strong background in bioinformatics and passion in exploring the heterogeneity and evolution of normal, progenitor, and tumor cells as well as the tumor microenvironment (TME) using state-of-the-art single-cell and spatial profiling approaches. The primary research focus of the candidate postdoc will be to employ innovative bioinformatics approaches to dissect in both human-relevant models of lung cancer as well as unique cohorts of human lung tissues the tumor "ecosystem" and profile in-depth phenotypic and functional heterogeneity, lineage plasticity and evolutionary dynamics of tumor and TME cells as well as cell-cell interactions in order to better understand lung cancer development, progression and therapeutic response. The postdoc candidate will work with close collaborators of the Kadara lab at MD Anderson and other institutions.

Please check the following link for more information on the Kadara lab: https://www.mdanderson.org/research/departments-labs-institutes/labs/kadara-laboratory.html

LEARNING OBJECTIVES

Learn and master skills for in-depth profiling of single-cell sequencing and spatial transcriptomics data. Learn and master skills for integrative analysis of high-dimensional bulk and single-cell multi-omics data. Gain rich knowledge in cancer biology, cancer genomics and immunogenomics, and cancer-specific bioinformatics. Develop skills in study design, data interpretation, presentation, and manuscript writing. Develop skills in networking, career planning, teaching, grant writing, and other professional development skills that can help the candidate fellow's career trajectory.

ELIGIBILITY REQUIREMENTS

Individuals with a PhD degree in computational biology, bioinformatics, data science or a related field are encouraged to apply. A strong computational background, proficiency in R and at least one additional scripting language (e.g., Python) and knowledge in biostatistics are required. Experience working with single-cell sequencing data and high-performance computing environment is highly preferred. A good understanding of cancer biology, immunology, cell biology, and molecular biology is also preferred. Proven track record of publications, excellent spoken and written communication skills are required. This appointment is not part of a clinical training program; individuals holding an M.D. degree or equivalent are not permitted to engage in patient care activity.

ADDITIONAL APPLICATION INFORMATION

https://www.mdanderson.org/research/departments-labs-institutes/departments-divisions/translational-molecular-pathology.html

POSITION INFORMATION

MD Anderson follows the NIH stipend levels as outlined by the Kirchstein - NRSA (https://grants.nih.gov/grants/guide/notice-files/NOT-OD-23-076.html) . This full-time trainee position will provide a salary between $56,484 to $68,604, dependent upon the years of postgraduate experience.

MD Anderson offers compensated trainees:

  • Paid medical benefits (zero premium) starting on first day for trainees who work 30 or more hours per week

  • Group Dental, Vision, Life, AD&D and Disability coverage

  • Paid Education Vacation and Sick Leave

  • Paid institutional holidays, wellness leave, childcare leave and other paid leave programs

  • Teachers Retirement System defined-benefit pension plan and two voluntary retirement plans

  • Employer paid life, AD&D and an illness-related reduced salary pay program

  • Health Savings Account and Dependent Care Reimbursement flexible spending accounts

  • Fertility benefits

  • State of Texas longevity pay

  • Extensive wellness, fitness, employee health programs and employee resource groups

FACULTY MENTOR

Humam Kadara

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