Lead Medical Imaging Researcher

9 hours ago

Shimoga, Karnataka, India beBeeData Full time ₹ 1,50,00,000 - ₹ 2,50,00,000
Unlocking Medical Imaging Breakthroughs

We are on a mission to revolutionize medical image diagnostics by harnessing the power of intelligent, holistic, and explainable care. Our team seeks innovative problem solvers who can empathize with consumers, understand business challenges, and design and deliver cutting-edge products.

Your primary focus will be applying deep learning and artificial intelligence techniques to medical image analysis. This involves selecting features, building and optimizing classifier engines using deep learning methods, understanding problems, and applying suitable image processing techniques.

You will work on complex problems related to medical image analysis, such as selecting features, designing solutions, and implementing best practices around statistical modeling. Additionally, you will develop and implement scalable data science architectures for training, inference, and deployment pipelines.

  • Selecting Features and Building Classifier Engines: Use deep learning techniques to select features and build classifier engines that optimize model performance.
  • Understanding Problems and Applying Image Processing Techniques: Apply image processing techniques to solve complex problems in medical image analysis.
  • Designing Solutions and Implementing Best Practices: Design and implement scalable data science architectures for training, inference, and deployment pipelines.
  • Developing Data Science Architectures: Develop and implement scalable data science architectures for training, inference, and deployment pipelines.
  • Ensuring Code Quality and Readability: Ensure code quality, readability, and maintainability by enforcing software engineering best practices within the data science team.
Required Skills and Qualifications
  • Strong Software Engineering Skills: Strong software engineering skills in Python (or other languages used in data science) with emphasis on clean code, modularity, and testability.
  • Deep Learning Techniques: Excellent understanding and hands-on experience with Deep Learning techniques such as ANN, CNN, RNN, LSTM, Transformers, VAEs etc.
  • Tensorflow or PyTorch Experience: Must have experience with Tensorflow or PyTorch framework in building, training, testing, and deploying neural networks.
  • Computer Vision Expertise: Experience in solving problems in the domain of Computer Vision.
  • Data Augmentation and Curation: Knowledge of data, data augmentation, data curation, and synthetic data generation.
  • Agile Methodologies: Exposure to Agile/Scrum methodologies and collaborative development practices.

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