Postdoctoral Fellow, Computational Biology in Cancer - Chan Lab at Memorial Sloan Kettering Cancer Center

The people of Memorial Sloan Kettering Cancer Center (MSK) are united by a singular mission: ending cancer for life. Our specialized care teams provide personalized, compassionate, expert care to patients of all ages. Informed by basic research done at our Sloan Kettering Institute, scientists across MSK collaborate to conduct innovative translational and clinical research that is driving a revolution in our understanding of cancer as a disease and improving the ability to prevent, diagnose, and treat it. MSK is dedicated to training the next generation of scientists and clinicians, who go on to pursueour missionat MSK and around the globe.

The Chan Lab in the Human Oncogenesis & Pathogenesis Program at Memorial Sloan Kettering Cancer Center is seeking a highly motivated and successful computational postdoctoral fellow to study the mechanisms and therapeutic implications of tumor cell plasticity.

Our lab in translational computational oncology integrates single-cell and spatial genomics, cell-free DNA profiling, and machine learning to understand how tumors evolve under therapeutic and microenvironmental pressures. We leverage large, deeply annotated patient cohorts with longitudinal sampling, tightly coupled with functional preclinical models, to enable discovery and validation of mechanisms of tumor evolution. A central focus is lineage plasticity, the ability of cancer cells to transition between phenotypic states, which underlies metastasis, immune evasion, and drug resistance.

Current research directions include:

• Defining transcriptional and epigenetic programs that drive lineage plasticity across adenocarcinoma, squamous, and neuroendocrine states in solid tumors

• Investigating analogous mechanisms of lineage switching in hematologic malignancies, including Richter's transformation and myelofibrosis.

• Mapping immune and treatment-induced pressures that shape cell fate decisions across space and time

• Building predictive computational models of lineage transformation and therapeutic resistance through multi-modal data integration (scRNA-seq, ATAC-seq, spatial imaging, lineage tracing, cfDNA)

This position offers the opportunity to work with deeply annotated patient cohorts, longitudinal samples, and cutting-edge experimental systems in a highly collaborative environment. We are seeking individuals who bring creativity, intellectual curiosity, and a strong drive to uncover underlying mechanisms, with a clear motivation to translate discoveries into improved patient care. Responsibilities include developing and applying computational methods, biological interpretation, assisting or leading in manuscript writing, engaging in a vibrant scientific community through regular work-in-progress and lab meetings, and other academic activities. For more information, please visit the lab website at www.joechanlab.org.

The ideal candidate should have:

• A PhD in Computational Biology, Bioinformatics, Computer Science, Statistics, or a related field

• Proficiency in Python and R

• Prior experience in computational biology

• Ability to engage in independent thinking

• Excellent skills in written and verbal communication

• Collegiality as a team member and a willingness to mentor other trainees

• At least 1 published first-author manuscript

If you have any questions about this job opportunity, please contact:

Dr. Joseph M. Chan, MD, PhD

chanj3@mskcc.org

Please include a cover letter, brief statement of prior research experience and research interests, CV, and contact information for three references in your application submission.

Salary: $72,000-$87,000 (depending on postgraduate experience)

Closing:

At MSK, we believe in fair, competitive pay that reflects your job, experience, and skills.

MSK is an equal opportunity and affirmative action employer committed to diversity and inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration without regard to race, color, gender, gender identity or expression, sexual orientation, national origin, age, religion, creed, disability, veteran status or any other factor which cannot lawfully be used as a basis for an employment decision.

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