This position is a part of the Yale Translational Image Analysis and Machine Learning Center. The goal of the center is to provide image analysis and machine learning support for research across multiple research groups and departments in a collaborative and interdisciplinary setting.
We are seeking someone who can assist scientific researchers in image analysis and drive the development of Machine Learning tools that focus around Radiology. The initial project will involve research in improving the understanding and characterization of the liver tumor microenvironment. The position will involve working in a multi-disciplinary research team that collaborates with many labs.
Quantitative Multimodal Image Guidance for Improved Liver Cancer Treatment
Description of Proposed Initial Project:
Liver cancer is one of the most common cancers worldwide and unlike all other cancers, its incidence continues to rise. Although loco-regional intra-arterial drug delivery has been shown to increase survival, it remains plagued by unresolved issues such as incomplete tumor kill that leads to high recurrence rates, absence of a standard protocol, and systemic toxicities. Our goal is to enhance our ability to deliver minimally invasive catheter-based treatments for liver cancer which simultaneously embolize the tumor vasculature and locally deliver chemotherapy agents, and assess their efficacy. At the core of these efforts are the development, evaluation and translation to clinical practice of advanced imaging and analysis methods to characterize the tumor microenvironment and derive feature information from novel multiparameter, multimodal images to classify tissue and quantify the response to therapy. The resulting methodology will lead to improved patient care through more precise delivery and more accurate response assessment.
The proposed project for the applicant is to drive the development of machine learning tools that can better characterize liver tumors from multi-parametric, multimodal, and multi-time point imaging data. The applicant will work closely with clinician researchers and biomedical engineers. This position will receive a competitive salary exceeding the typical compensation for graduate students in this field.
- Bachelor’s degree in engineering (i.e. Biomedical, Electrical, etc.), computer science, data science or equivalent. Master’s degree preferred.
- Experience in machine learning and statistics.
- Experience in medical imaging research.
- Programming experience in MATLAB and Python.
- Publication record and coursework in physiology are pluses.
- Can commit to at least 12 months for this position, with potential to extend