FACULTY POSITION IN COMPUTATIONAL ONCOLOGY, MEMORIAL SLOAN KETTERING CANCER CENTER
2 weeks ago
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 Computational Oncology Program at Memorial Sloan Kettering Cancer Center (MSK) is inviting applications for tenure track faculty positions. We are seeking exceptional candidates to join our faculty and develop independent investigator-led, computationally-focused cancer research programs. has grown to ten faculty members who have created a vibrant, dynamic and collaborative scientific community in the field of computational oncology. Successful candidates will be supported to develop world-leading independent scientific programs to advance our understanding of cancer biology and conduct next generation translational research driven by data science, artificial intelligence, machine learning, computer science, computational biology and other quantitative fields. You will have the opportunity to collaborate with exceptional clinician-scientists, generate unique experimental data, analyze unique real-world datasets and to study biospecimens derived from the MSK patient population.
Areas of particular interest include but are not limited to:
- Single cell dynamics of tumors
- Anti-tumor immunity and immuno-oncology
- Drug sensitivity and resistance mechanisms
- Computational pathology, medical and cellular imaging
- Large language models for cancer data
- Multimodal data integration (e.g. genomics with clinical data & pathology images)
- Longitudinal analysis of real world data from patients
- The cancer microbiome
- Computational method development for any of the above areas
- Other emerging areas in cancer research
Eligible candidates will have a PhD or MD/PhD in Bioinformatics, Computational Biology, Computer Science, Mathematics, Statistics or a related discipline. Candidates with strong backgrounds in machine learning/AI, algorithm development, mathematical modeling, data science or data-driven discovery are strongly encouraged to apply.
Our program is located at the newly created Computational Oncology Research Campus of MSK on the Upper East Side of New York, in close proximity to Memorial Hospital, Weill Cornell Medical College, and Rockefeller University. Our faculty are eligible to hold appointments in the , the , and the program across MSK and WCMC, providing access to exceptional graduate students. We seek candidates committed to advancing equity, diversity, and inclusion () in our research community. At MSK, diversity encompasses the variety of personal experiences, values and worldviews that result from differences in race, ethnicity, gender, age, religion, national origin, language, abilities/disabilities, sexual orientations, socioeconomic status, geographic region and more.
Please send your cover letter, curriculum vitae, a summary of current and proposed research programs, and arrange for three letters of recommendation to be sent to: Dr. Sohrab Shah, Chief of Computational Oncology, c/o Cristina Radu, Memorial Sloan Kettering Cancer Center, 321 East 61st Street 4th floor, New York, NY 10065; E-mail: CompOncBST@mskcc.org.
Applications should be received by 12/01/2023. To learn more about recent advances from our Computational Oncology labs, please visit: .
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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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