Research Fellow

Overview

The Candidate will work in the exciting and dynamic fields of deep learning (DL) and digital pathology, and report directly to Christopher Bridge and Albert Kim [Quantitative Translational Imaging in Medicine (QTIM) Lab (https://qtim-lab.github.io) at the Athinoula A. Martinos Center for Biomedical Imaging] and A. John Iafrate of the Department of Pathology.

The Candidate’s main focus will be to train DL algorithms as applied to digital pathology data in order to develop improved cancer biomarkers and study therapeutic resistance. The Candidate will conduct original research aligned with the laboratory’s research agenda but with significant scope for self-directed research. The main focus of this position will be on the application of deep learning to a large dataset of multiplexed immunofluorescence images from patients enrolled in a successful phase 2 clinical trial demonstrating checkpoint inhibitor efficacy in brain metastases of diverse histologies (Brastianos & Kim et al., Nature Medicine 2023). These results should produce biologically-relevant insights and address technical challenges including maximizing interpretability.

In this role, the Candidate will work in a highly collaborative environment with computer scientists, machine learning scientists, and physician-scientists at the Massachusetts General Hospital (MGH). There will be opportunities for technical innovation on these projects, as well as collaborations with physician-investigators (Priscilla Brastianos) from the MGH Cancer Center. The ideal candidate will have both expertise in state-of-the-art deep learning methodologies and experience of the specific challenges of applying them to medical imaging, as well as a strong track record of scientific publications. In our group, we have a history of publications in high-impact journals, and our alumni have a track record of independent faculty positions and impactful positions in industry.

· Conduct research projects in the area of medical image analysis with deep learning, with a primary focus on digital histopathology and multiplexed immunofluorescence.

· Assist graduate students, medical students and interns in their research projects.

· Prepare articles and abstracts for high impact scientific journals and conferences.

· Propose and execute novel research projects, in preparation for developing an independent research agenda within the field of medical image analysis.

· Assist with the preparation of applications for research grants.

- PhD in a field related to medical image analysis, such as computer science, engineering, applied mathematics, or physics

- Excellent computing abilities including coding in Python, use of deep learning frameworks such as pytorch and tensorflow, and use of Linux-like computing environments.

- Well-developed organizational, analytical, and interpersonal skills.

- Ability to communicate complex technical ideas in verbal/written and written forms.

- Expertise in state-of-the-art deep learning

- Previous experience working in at least one area of medical image analysis.

- Well-developed organizational and analytical skills, excellent verbal/written communication and interpersonal skills are required.