Engage in cutting-edge AI research as a Research Intern, focusing on developing and refining AI algorithms to enhance medical workflows. This role offers a unique opportunity to collaborate closely with experienced technologists and engineers, contributing to innovative solutions for complex, multidisciplinary challenges at GE Healthcare.

GE HealthCare is a global leader in medical technology and digital solutions, driven by a mission to create a world with limitless healthcare possibilities. Join an environment where your ambition is valued, your ideas can transform into impactful realities, and every contribution helps build a healthier future.

Contribute to technology development and research in AI algorithms for medical workflow improvements.

Focus on specific areas including image analytics, deep learning, inverse problems in imaging, clinical decisioning, and super-resolution reconstruction.

Apply expertise in deep learning and computer vision, utilizing frameworks like TensorFlow, PyTorch, and Keras.

Gain experience with generative AI techniques such as diffusion models, GANs, foundation vision models, multimodal models, and large language models (LLMs).

Implement generative AI solutions for medical imaging use cases, including image synthesis, reconstruction, report generation, data augmentation, segmentation, and workflow automation.

Explore familiarity with vision-language models (VLMs), retrieval-augmented generation (RAG), and multimodal AI systems within healthcare.

Currently pursuing a PhD in Computer Science, Data Sciences, or AI from a reputable institution.

Demonstrated understanding of building large-scale AI models, including generative AI, large vision/language models, and multimodal AI for tasks like segmentation, detection, and classification.

Practical implementation experience with high-level languages such as Python and C++.

Keen interest in staying updated with the latest state-of-the-art algorithms and emerging technologies.

Proven ability to navigate and address challenges associated with vaguely defined or abstract problems.

Proficiency with frameworks and tools including Keras, HuggingFace, Vector databases, PyTorch, and TensorFlow.

Experience in training large-scale AI models.

In-depth knowledge of machine learning algorithms and modeling techniques, such as semi-supervised learning, generative models, transfer learning, and optimization.

Capacity to work effectively both independently and as part of a collaborative team.