Postdoctoral Fellow - Thoracic Head & Neck Medical Oncology - Research
Listed on 2026-10-15
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Research/Development
Research Scientist, Postdoctoral Research Fellow, Biomedical Science, Data Scientist
Join our dynamic team of researchers focused on the progression from lung precancer to invasive malignancies. We collaborate with multidisciplinary investigators to develop innovative early detection and treatment strategies.
We are currently seeking postdoctoral fellows with expertise in either computational/bioinformatics research or experimental/wet-lab cancer research.
As a highly motivated postdoctoral fellow, you'll contribute to cutting-edge research under the mentorship of Dr. Jianjun Zhang, M.D., Ph.D., a tenured Professor with a proven track record of funding success. You'll have the opportunity to:
- Publish high-impact papers in top-tier journals.
- Gain hands-on experience with advanced computational or experimental research approaches.
- Contribute to groundbreaking discoveries that advance lung cancer research.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
LEARNING OBJECTIVES- Investigate the molecular, genomic, and immunological mechanisms underlying lung cancer initiation, progression, and therapeutic response.
- Apply advanced computational or experimental approaches to study lung cancer biology, tumor heterogeneity, and the tumor microenvironment.
- Collaborate with scientists and clinicians to develop innovative strategies for lung cancer early detection, interception, and treatment.
- Present research findings at scientific conferences and publish in high-impact journals.
- Ph.D. or M.D. in cancer biology, molecular biology, genetics, immunology, bioinformatics, computational biology, biostatistics, or a related field.
- Demonstrated research experience in either computational/bioinformatics research or experimental/wet-lab cancer research.
- Ability to work independently and as part of a team.
- Passion for research and a commitment to advancing the field of lung cancer research.
- Experience analyzing high-throughput sequencing data, particularly single-cell RNA sequencing (scRNA‑seq) and spatial transcriptomic data.
- Demonstrated ability to independently analyze and interpret large-scale genomic datasets.
- Proficiency in R and/or Python is strongly preferred.
- Experience in cancer genomics, multi-omics integration, tumor immunology, or analysis of the tumor microenvironment is highly desirable.
- Strong training in basic and/or translational cancer research, with substantial experience using mouse models of cancer.
- Experience in cancer biology, molecular and cellular biology, tumor immunology, or characterization of the tumor microenvironment is highly desirable.
- Experience with single-cell, spatial, or other high-throughput profiling technologies is a plus.
- Strong research productivity and a record of peer-reviewed publications are preferred.
Learn more about Dr. Zhang's recent publications: https://www.mdanderson.org/research/departments-labs-institutes/labs/zhang-laboratory/publications.html
POSITION INFORMATIONOffsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law.
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