Embark on an exciting journey as a Research Intern focused on Artificial Intelligence. You will play a key role in developing and researching cutting-edge AI algorithms designed to enhance medical workflows. This position offers a unique opportunity to collaborate closely with experienced technologists and engineers, contributing to innovative solutions for complex, multidisciplinary challenges within GE Healthcare.

GE HealthCare is a global leader in medical technology and digital solutions, driven by a mission to create a world where healthcare knows no boundaries. Join an organization where your contributions are valued, and together, we can transform ideas into world-changing realities, building a healthier future for all.

Key areas of focus include image analytics, deep learning, and inverse problems in imaging, with applications in clinical decisioning and super-resolution reconstruction.

You'll leverage expertise in deep learning, computer vision, and image processing, utilizing frameworks like TensorFlow, PyTorch, and Keras.

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

Apply generative AI to medical imaging use cases, including image synthesis, reconstruction, report generation, data augmentation, segmentation, and clinical workflow automation.

Familiarity with vision-language models (VLMs), retrieval-augmented generation (RAG), and multimodal AI systems tailored for healthcare applications is beneficial.

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

Demonstrate a solid understanding of building large-scale AI models, including generative AI, large vision/language models, and multimodal AI for segmentation, detection, quantification, classification, and more.

Possess implementation experience with programming languages such as Python and C++.

Stay abreast of state-of-the-art algorithms and competing technologies.

Showcase experience and capability in handling abstract or vaguely defined problems.

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

Experience in large-scale AI training is required.

An in-depth understanding of machine learning algorithms and modeling, including semi-supervised learning, generative models, transfer learning, optimization, and large language models.

Exhibit the ability to work effectively both independently and as part of a team.