Senior Scientist – Image Analytics

GE Healthcare GE Healthcare · Healthcare · Bengaluru, Karnātaka, India · Digital Technology / IT

Senior Scientist in AI/ML Domain focused on building early-stage AI-driven solutions in Image Analytics and Computer Vision for healthcare products. The role involves applied research, developing and optimizing deep learning algorithms for medical imaging, implementing advanced AI techniques like generative AI and foundation models, and building proof-of-concepts. Collaboration with cross-functional teams and transition of research to product development are key aspects.

What you'd actually do

  1. Conduct applied research and develop innovative AI, computer vision, and image analytics solutions for next-generation healthcare products, software, and services, with a focus on early-stage (low TRL) technologies.
  2. Design, develop, and optimize machine learning, deep learning, and image processing algorithms for medical imaging modalities such as MRI, CT, Ultrasound, X-ray, and physiological monitoring systems.
  3. Research and implement advanced AI techniques, including generative AI, diffusion models, GANs, foundation vision models, multimodal models, and large language models (LLMs), to solve challenging healthcare imaging and clinical workflow problems.
  4. Develop physics-informed and data-driven AI models for applications such as image reconstruction, enhancement, segmentation, detection, registration, super-resolution, and clinical decision support.
  5. Build proof-of-concepts and technology prototypes to validate new ideas, assess technical feasibility, and demonstrate the potential value of emerging AI technologies.

Skills

Required

  • Deep Learning
  • Computer Vision
  • Image Processing
  • TensorFlow
  • PyTorch
  • Keras
  • Generative AI techniques
  • diffusion models
  • GANs
  • foundation vision models
  • multimodal models
  • large language models (LLMs)
  • physics-informed and data-driven AI models
  • image reconstruction
  • enhancement
  • segmentation
  • detection
  • registration
  • super-resolution
  • clinical decision support
  • proof-of-concepts
  • technology prototypes

Nice to have

  • vision-language models (VLMs)
  • retrieval-augmented generation (RAG)
  • multimodal AI systems
  • medical imaging
  • biology
  • design, analysis, and implementation of algorithms in different computing architectures

What the JD emphasized

  • early-stage (low TRL)
  • applied research
  • generative AI
  • foundation vision models
  • multimodal models
  • LLMs
  • physics-informed and data-driven AI models
  • image reconstruction
  • enhancement
  • segmentation
  • detection
  • registration
  • super-resolution
  • clinical decision support
  • proof-of-concepts
  • technology prototypes
  • PyTorch
  • TensorFlow
  • Keras

Other signals

  • applied research
  • develop innovative AI
  • computer vision
  • image analytics
  • early-stage (low TRL)
  • generative AI
  • foundation vision models
  • multimodal models
  • LLMs
  • physics-informed and data-driven AI models
  • image reconstruction
  • enhancement
  • segmentation
  • detection
  • registration
  • super-resolution
  • clinical decision support
  • proof-of-concepts
  • technology prototypes
  • PyTorch
  • TensorFlow
  • Keras