Postdoc Opportunity at MSK: Research Scholar, Computational Oncology/Immuno Oncology

Memorial Sloan Kettering Cancer CenterNew York City, New YorkOn-siteFull-timeListed 8 hours ago

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About this role

About Us:

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.

We are seeking a highly motivated computational biology postdoctoral fellow to lead translational studies in single cell and spatial biology of gastric cancer immunology and evolution. Our investigator group, led by Drs Santosh Vardhana (tumor immunology), Sohrab Shah (computational oncology) and Vivian Strong (gastric cancer surgery) has established a platform that enables longitudinal acquisition of tumor, normal mucosa, draining lymph nodes, metastases, and peripheral blood from patients with gastrointestinal cancers and pre-malignant lesions, including in the context of neoadjuvant therapies.  This pipeline has generated a multimodal immune profiling dataset of cancer patients treated with immunotherapy, including flow cytometry, scRNA-seq, spatial transcriptomics, and co-registered multiplex immunofluorescence (mIF) and H&E whole-slide images, all linked to comprehensive clinical annotations.  This is complemented with mechanistic mouse models to functionally establish immune and tumor co-evolutionary networks in gastric tumors, tumor draining lymph nodes and metastases. We are seeking highly motivated candidates with advanced computational biology skills in the technical areas of single cell and spatial omics, ideally with some background or advanced knowledge of tumor immunology.  The position is funded by a 5yr R01 newly awarded to our investigator group.

The successful candidate will have the opportunity to lead the science for integrating multimodal datasets addressing fundamental unanswered questions in gastric cancer biology, and addressing the urgent clinical unmet need plaguing patients afflicted with gastric cancer.  The fellow will work closely with, and will be mentored by, world-leading experts in computational oncology, immunology, pathology, GI oncology & surgery, with opportunities to develop both methodological innovations and lead discovery-based research.

Profiles of the co-mentors of this role are listed below:

https://www.mskcc.org/research-areas/labs/sohrab-shah

https://www.mskcc.org/research-areas/labs/santosha-vardhana

Key Requirements:

- Hold a Ph.D. in biostatistics, statistics, computer science, computational biology, or related quantitative discipline.
- Have published a 1st author paper from PhD dissertation or postdoctoral research

Core Skills:

- PhD-level experience in computational biology, single cell omics applied in a cancer setting
- Experience in high-dimensional data analysis
- Preferably have advanced knowledge of topics in tumor biology and immunology

Submit C.V., cover letter, and 3 references in your application submission to:  Cristina Radu [email protected]

Salary range $75,000 - $80,000 based on PGY level

Pay Range: $0.00 - $10,000,000.00
FSLA Status: Exempt

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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