H& E Image Analysis Specialist

15 hours ago

Vijayawada, Andhra Pradesh, India beBeeMachineLearning Full time ₹ 15,00,000 - ₹ 25,00,000

Job Opportunity: H&E Image Analysis Specialist

We are seeking a skilled and motivated candidate to join our team as an H&E image analysis specialist. The ideal candidate will have a strong background in machine learning and experience with deep learning models.

This role involves the design, development, and training of convolutional neural networks (CNNs) and related machine learning models on H&E-stained histology images. The successful candidate will contribute to the development and optimization of these models to analyze digitized H&E slides for cancer classification and spatial mapping.

The successful candidate will work collaboratively with cross-disciplinary teams to integrate image-based predictions with molecular and clinical data. They will also be responsible for analyzing model performance and contributing to improving accuracy, efficiency, and robustness.

Responsibilities:

  • Design, develop, and train CNNs and related ML models on H&E-stained histology images.
  • Use and extend tools such as QuPath for cell annotations, segmentation models, and dataset curation.
  • Preprocess, annotate, and manage large image datasets to support model training and validation.
  • Collaborate with cross-disciplinary teams to integrate image-based predictions with molecular and clinical data.
  • Analyze model performance and contribute to improving accuracy, efficiency, and robustness.
  • Document research findings and contribute to publications in peer-reviewed journals.

Requirements:

  • PhD in Computer Science, Biomedical Engineering, Data Science, Computational Biology, or a related discipline.
  • Demonstrated research experience in machine learning, deep learning, or biomedical image analysis (e.g., publications, thesis projects, or conference presentations).
  • Strong programming skills in Python and experience with ML frameworks such as TensorFlow or PyTorch.
  • Familiarity with digital pathology workflows, image preprocessing/augmentation, and annotation tools.
  • Ability to work collaboratively in a multidisciplinary research environment.

Preferred Qualifications:

  • Background in cancer histopathology or biomedical image analysis.
  • Knowledge of multimodal data integration, including spatial transcriptomics.

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