Postdoctoral fellow– Developing methods for longitudinal response monitoring and predicting treatment toxicity in patients with lung cancers

Job details

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.

Role Overview:

An opening is now available for a postdoctoral researcher in the department of medical physics for individuals interested in developing deep learning methods to combine longitudinal information to predict treatment response and toxicities to identify patients likely to benefit from treatment adaption in patients with lung cancers. The two-year project involves enhancing existing and developing new methods to integrate multiple sources of information including clinical text, diagnostic CTs, and weekly cone beam CTs to derive biomarkers of treatment response and normal tissue toxicity. This is a unique opportunity to work with a well-curated database consisting of a large number of high-quality longitudinal datasets of lung cancer patients undergoing systemic and concurrent radiation treatments.

The project will involve working with a multi-disciplinary team of computer scientists, radiation oncologists, medical physicists, and oncologists. This is an excellent opportunity for a motivated researcher with a PhD in computer science, biomedical engineering, electrical engineering, medical physics or related fields, interested in developing novel AI solutions and work with a clinical team that translates advanced AI solutions for clinical cancer care. Our team develops new AI methods for various problems involved in radiotherapy ranging from autosegmentation to treatment outcomes and toxicity prediction. Our MSK-AI autosegmentation tools are routinely used for radiation treatment planning from brain to pelvis. The project if successful will have the potential to individualize treatments for improving survival and quality of life outcomes for patients with cancer.

Department Overview
The Department of Medical Physics consists of over eighty faculty physicists and computer scientists plus support staff working on various physical problems related to diagnosis and therapy for cancer, in partnership with radiologists, radiation oncologists, and other medical professionals. The Medical Physics department has a strong culture of research and development and clinical implementation. Research efforts include the development of cutting edge AI methods for longitudinal tumor treatment response assessment, tumor and tissue segmentation, image reconstruction, MR fingerprinting, real-time motion tracking, and adaptive radiation therapy. Medical Physics clinical deployment team involves various disciplines including AI governance focused on rigorous AI model evaluation, commissioning and online quality assessments, combined with excellent computing infrastructure and software tools for development and deployment of new methods.

MSK is also a World leader in imaging and theranostic research and supports one of the largest pre-clinical small animal core facilities in the United States that includes MR, PET, SPECT, CT, ultrasound imaging equipment that provides a conduit for the translation of experimental techniques into clinical trials. Additional highlights include grant-funded research in quantitative MRI, hypoxia imaging, imaging drug delivery, and radionuclide dosimetry. Diagnostic, Nuclear and MRI Medical Physicists play a key role in supporting all imaging devices throughout the MSK enterprise, maintaining and harmonizing acquisition protocols, troubleshooting image artifacts, and maintaining equipment accreditation and regulatory compliance. We support well over 500 imaging instruments that include 40 CT scanners, 21 MR scanners, and 17 PET scanners and work with radiologists in the selection of new imaging equipment and the implementation of new imaging techniques. Since diagnostic images are at the core of cancer diagnosis and response assessment, we have a strong philosophy to ensure that all imaging equipment throughout the network is operating at the optimum performance, per the overall mission of MSKCC to advance the state of cancer care.

Key Requirements:

• PhD in Computer Science, Biomedical Engineering, Electrical Engineering, Physics, or related fields.
• Knowledge of machine learning including deep learning and/or medical image analysis is required.
• Strong programming skills (Python, Keras.) is required.
• Computer vision -based applications is highly desirable.
• Prior experience in MRI image analysis, multi-modal data analysis is a plus.
• Excellent verbal and written communication skills are required.
• Ability or interest in working with a multi-disciplinary team.

Contact:

If you have any questions about this job opportunity, please contact:
Harini Veeraraghavan, PhD
veerarah@mskcc.org
 

Salary: $72,000 - $93,730

Salary will be commensurate with experience and the cost of living in New York City. Subsidized housing close to the MSK campus is available based on availability.

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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