Research Intern - AI

GE Healthcare GE Healthcare · Healthcare · Bengaluru, Karnātaka, India · Engineering / Technology

Research intern focused on AI algorithms for medical workflows, with expertise in image analytics, deep learning, generative AI (diffusion models, GANs, foundation vision models, LLMs), multimodal models, VLMs, and RAG for healthcare applications. Requires PhD pursuit, experience with TensorFlow, PyTorch, Keras, HuggingFace, and large-scale AI training.

What you'd actually do

  1. Specific areas of required expertise are: Image analytics, Deep Learning, Inverse problems in Imaging, Clinical decisioning, super resolution reconstruction.
  2. Expertise in Deep Learning and Computer Vision/ Image processing, TensorFlow, Pytorch and Keras is required.
  3. Experience with Generative AI techniques including diffusion models, GANs, foundation vision models, multimodal models, and large language models (LLMs).
  4. Experience applying generative AI to medical imaging use cases such as image synthesis, image reconstruction, report generation, data augmentation, segmentation, and clinical workflow automation.
  5. Familiarity with vision-language models (VLMs), retrieval-augmented generation (RAG), and multimodal AI systems for healthcare applications.

Skills

Required

  • Image analytics
  • Deep Learning
  • Computer Vision
  • Image processing
  • TensorFlow
  • PyTorch
  • Keras
  • Generative AI
  • Diffusion models
  • GANs
  • Foundation vision models
  • Multimodal models
  • Large language models (LLMs)
  • Vision-language models (VLMs)
  • Retrieval-augmented generation (RAG)
  • Python
  • C++
  • HuggingFace
  • Vector databases
  • Machine learning algorithms
  • Semi-supervised learning
  • Weakly supervised learning
  • Generative models
  • Transfer learning
  • Optimization

Nice to have

  • Inverse problems in Imaging
  • Clinical decisioning
  • Super resolution reconstruction
  • AI safety filtering
  • LLM observability
  • Agent orchestration
  • Tool use
  • Evals
  • Guardrails
  • Model serving
  • Recommender systems
  • Search ranking
  • Audio speech
  • Frontier research
  • Interpretability
  • Synthetic data
  • Agent research
  • RLHF
  • RLAIF
  • Reward modeling
  • RL robotics
  • Embodied AI

What the JD emphasized

  • Currently pursuing a PhD in Computer Science / Data Sciences / AI from a reputed institute
  • Experience with frameworks and tools such as Keras, HuggingFace, Vector databases, PyTorch, Tensorflow etc.
  • An in-depth understanding of machine learning algorithms and modeling (e.g., semi-supervised or weakly supervised learning, generative models, transfer learning, optimization, large language models, etc.)

Other signals

  • AI algorithms for improving medical workflows
  • novel solutions for complex, multi-disciplinary problems
  • Deep Learning and Computer Vision/ Image processing
  • Generative AI techniques including diffusion models, GANs, foundation vision models, multimodal models, and large language models (LLMs)
  • applying generative AI to medical imaging use cases
  • vision-language models (VLMs), retrieval-augmented generation (RAG), and multimodal AI systems for healthcare applications
  • building large scale AI such as generative AI models, large vision/language models, and multi-modal AI models
  • large scale AI training