Conceive, develop, and implement analytics and AI/Machine learning radiology solutions to high-priority scientific problems
Participate in cross-functional collaborations with external companies and internal scientific, clinical, and data science teams
Shape internal/external collaborations and define the scope of research questions
Extract insight from complex, unstructured radiology image or other imaging modalities data to enhance decision making in clinical development of cancer therapy (e.g., tumor characterization, outcome prediction, etc.)
Clearly articulate highly technical methods and results to diverse audiences and partners to drive decision-making

Requirements

Ph.D. degree in a quantitative discipline (e.g., artificial intelligence, computer science, applied mathematics, electrical engineering, or similar), completed within the past 3 years, or to be completed within the next 6 months.
Proficiency in radiology imaging modalities such as CT, PET, MRI, etc.
Demonstrated experience driving research in and applying Computer Vision techniques, such as Foundation models, Transformers, CNNs, Diffusion models, RNNs, GANs, and radiomics.
Extensive experience with traditional Computer Vision applications, such as OpenCV, object detection, edge detection, image segmentation.
Extensive hands-on experience with medical imaging modalities and formats (e.g., DICOM, nrrd, nifti) and collaborating with subject matter experts.
Proficiency with one or more programming languages such as Python or C++
Strong publication record and ability to effectively communicate technical work to a wide audience.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AIMachine LearningComputer VisionFoundation modelsTransformersCNNsDiffusion modelsRNNsGANsRadiomics

Soft Skills

collaborationcommunicationarticulationdecision-making

Certifications

Ph.D. in quantitative discipline