PhD Candidate (4yr)/Postdoc (3yr) in Deep Learning-Based Multimodal Image Analysis for Breast Cancer Risk Assessment, Therapy Response, and Surveillance

Project Overview

Breast cancer remains a significant health concern for women worldwide. Early detection and advanced treatment strategies are vital for enhancing patient outcomes. By harnessing AI in breast imaging, we aim to transform breast cancer diagnostics and optimize treatment planning. This project is centered on using deep learning to design personalized strategies for breast cancer detection and treatment, aiming to diminish the disease's societal and personal implications.

While our primary emphasis is on MRI-recognized as the most sensitive breast imaging method-we'll evaluate diverse imaging modalities. Our objective is to enhance personalized breast cancer care by deploying AI to analyze longitudinal data, concentrating on the development of interpretable AI models that improve trust and ensure smooth integration in clinical environments.

This initiative is a collaborative effort involving:

• Netherlands Cancer Institute (Amsterdam)
• Radboud University Medical Center (Nijmegen, Netherlands)
• Memorial Sloan Kettering Institute (New York, USA)
• ScreenPoint Medical (Nijmegen, Netherlands)

Job Responsibilities

As a PhD candidate, you'll focus on:

• Developing cutting-edge deep learning methodologies for multimodal data to assess breast cancer risk, therapy response and monitor post-operative cancer recurrence
• Ensuring these algorithms validate across standalone cases to ensure their clinical relevance together with a clinical PhD candidate

You'll be part of

• The AI for Oncology Group at the Netherlands Cancer Institute (Jonas Teuwen) https://aiforoncology.nl
• The Breast Imaging Group at the Netherlands Cancer Institute & Radboud University Medical Center (Ritse Mann) In addition, you will be in close contact with researchers at the (Memorial Sloan Kettering)

AI for Oncology Lab: Dedicated to developing AI solutions to enhance cancer diagnostics and treatments via methodological advancements and expert collaborations. We have a wide range of deep learning-based projects ranging from fundamental AI research to translational evaluation of existing algorithms. Check our website for more information.

Breast Imaging Group (BIG): A clinical research group aimed at minimizing the breast cancer burden. Our focus spans from evaluating existing imaging techniques to the development of AI in medical breast imaging, coupled with the exploration of minimally invasive therapy and understanding psychosocial dimensions of novel methods.

You will also have an opportunity to spend several months at the Memorial Sloan Kettering Institute in New York.

Candidate Profile

We seek an ambitious, autonomous, and proactive PhD candidate or postdoc passionate about contributing to a cross-disciplinary environment. Your role will involve developing innovative deep learning models and steering research in collaboration with project leads and experts.

Requirements

• A Master's degree in artificial intelligence, computer science, physics, mathematics, or a similar field.
• Proficiency in deep learning and exceptional programming skills.
• Clear evidence of deep learning proficiency, reflected through coursework and GitHub repositories.
• An openness for a research visit to the Memorial Sloan Kettering in New York
• Postdoctoral candidates must have extensive expertise in deep learning for medical image analysis.

We welcome applications which do not fully qualify these requirements, but make sure you argue why you are suitable for this position in your application letter. Please be aware that a master’s degree is required in the Netherlands to obtain a PhD degree.

Application Process

For inquiries or more about this position, reach out to Dr. Jonas Teuwen at j.teuwen@nki.nl or Dr. Ritse Mann at r.mann@nki.nl.

Submit your application letter, resume, and course list to Jonas Teuwen at j.teuwen@nki.nl with the subject line "APPLICATION: aiEMBRACE AI position". Incomplete applications will be directly rejected without feedback. The position will remain open until filled, and applications are reviewed immediately upon arrival.