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 pursue our mission at MSK and around the globe. 

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The Chan Lab in the Human Oncogenesis & Pathogenesis Program at Memorial Sloan Kettering Cancer Center is seeking a highly motivated and successful individual with a background in computational biology. The Chan Lab is a new, multidisciplinary group developing machine learning methods using cutting-edge single-cell and cell-free sequencing as well as spatial imaging to study how cell fate decisions mediate metastasis and acquired resistance across different cancer types, and how epigenetic and environmental factors guide this process. A central focus of our work is to quantify lineage plasticity (the capacity to switch to non-canonical phenotypes) to gain insight into the underlying molecular machinery and identify new drug targets to constrain or even reverse plasticity. Representative publications include:    
 

1. Chan, et al. Lineage plasticity in prostate cancer depends on JAK/STAT inflammatory signaling. Science, 2022. 

1. Chan, et al. Signatures of plasticity, metastasis, and immunosuppression in an atlas of human small cell lung cancer. Cancer Cell, 2021. 

1. Schoenfeld,  Chan, et al. Tumor analyses reveal squamous transformation and off-target alterations as early resistance mechanisms to first-line osimertinib in EGFR-mutant lung cancer. Clinical Cancer Research, 2020.  

1. Offin,  Chan, et al. Concurrent RB1 and TP53 alterations define a subset of EGFR-mutant lung cancers at risk for histologic transformation and inferior clinical outcomes. Journal of Thoracic Oncology, 2019.  

1. Chan, et al. Topology of viral evolution. PNAS, 2013.  

1. Singh,  Chan, et al. Transforming Fusions of FGFR and TACC Genes in Human Glioblastoma. Science, 2012.  

This position will involve a mixture of methodological development and analysis of large-scale datasets of patient samples from a leading cancer center, as well as time-series analysis of sophisticated preclinical models through highly collaborative research. This position will require developing and maintaining software packages, 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 further 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: $70,000-$85,000 (depending on postgraduate experience)  

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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