Machine Learning Research Intern - Foundation Models for Radiology

Foundation models are pre-trained on large amounts of data and can be adapted to many downstream tasks with few or no labels. At Raidium, we build our own foundation models for radiology, for 3D CT and MRI imaging. We released Curia, a state-of-the-art vision encoder for CT and MRI, trained on about 150,000 clinical exams. We then built Jolia, a vision-language model for 3D CT that learns from radiology reports at the level of individual findings. Our next steps include localization, multi-volume and longitudinal reasoning, and more interactive use of these models by radiologists. As an intern, you join the research team and work on one research topic related to foundation model pre-training and adaptation. In particular, you will work development of generative VLMs with image grounding and localisation, with applications to findings classification and evolution of pathologies through time. In this internship, you will: Read the latest literature on foundation models research for vision and language Design and run experiments on 3D vision and vision-language foundation models for radiology Train models at scale on Raidium's dataset of radiology exams and reports, using GPU clusters Benchmark your approach on clinical downstream tasks (detection, segmentation, classification, report generation) Work with radiologists to define the needs and to check the results Share your results with the team, and when possible, publish them in a top-tier conference or journal You will have a daily follow-up with a researcher from the team, and a weekly meeting with senior researchers. The internship will be managed by a lead researcher at Raidium. About Raidium Raidium builds foundation models for radiology and the software that brings them to radiologists. Our models, Curia and Jolia, power a new generation of tools to read and analyze CT and MRI exams. Raidium was founded by experts in radiology and machine learning: Pierre Manceron, CEO, Centrale Paris engineer in Applied Mathematics and Machine Learning, former ML lead at Owkin Paul Herent, CMO, radiologist and former interne des hôpitaux de Paris, Cogmaster (ENS) and PSL, with published research on deep learning in radiology