Machine learning and deep learning are changing how cancer patients are diagnosed and treated in modern hospitals. Computational Pathology leverages large-scale machine learning on high performance compute infrastructure to transform pathology from a qualitative to quantitative science. You will work in an exciting interdisciplinary environment with the potential to directly impact medical research, cancer care and patients lives.
For our computational pathology group, we are looking for a Machine Learning Scientist who will lead in the development, training and testing of Machine Learning which will aid in the clinical assessment and understanding of cancer. The massive amount of digital pathology image data at MSKCC provides a unique opportunity within the field of computer vision and cancer research for conventional and unconventional modeling with clinical relevance, such as (semi-)supervised, weakly supervised and unsupervised machine learning methods.
Work and collaborate with a diverse team of machine learning experts, software engineers and medical doctors to build a new generation of artificial intelligence in cancer detection and treatment
Employ statistical methodologies on high volumes of data to solve novel problems.
Build software and create actionable insights.
Work at a high level of complexity in relation to image data, deep learning, and/or computational pathology and know statistical programming languages including but not limited to: R, Python, & Matlab.
Have the opportunity to leverage a modern compute cluster with hundreds of GPU’s and the largest cluster of DGX nodes in the field.
Doctorate in Computer Science with an emphasis on Machine Learning or Computer Vision.
An outstanding publication history in machine learning and/or computational pathology which includes a track record with established machine learning conferences, e.g., MICCAI, CVPR, ICML, etc.
Strong knowledge and background in machine learning, deep learning, computer vision and/or medical imaging.
Experience in high performance computing (HPC).
Ability to excel working both independently and within a team, possessing a collaborative research mindset allowing them to work comfortably together with pathologists, AI researchers (same field and other fields), and computer scientists.
Ability to offer mentorship and guidance to others.
Interest in the independent development and testing of hypotheses and are creative in finding both conventional and unconventional solutions, e.g., you don’t stop with the outcome of a model, but also make interpretations and connect results with business needs.
Savvy at tackling deeper questions and exploring new approaches.
Interest in medical data analysis.
Maintains and enhances professional growth and development through participation in scientific and technical discussions, workshops, and seminars to keep current with developments in web technology and computational tools.