Machine Learning Engineer Position in Biomedical Image Analysis

2 days ago

Erode, Tamil Nadu, India beBeeBiomedical Full time ₹ 90,00,000 - ₹ 1,20,00,000

Job Title: H&E Image Analysis Scientist / Machine Learning Engineer-Spatial Omics (PhD)

We are seeking a motivated PhD candidate with expertise in machine learning for histopathology image analysis. This is an excellent opportunity to contribute to the development and optimization of deep learning models for cancer classification and spatial mapping.

The ideal candidate will have a strong background in biomedical engineering, data science, or a related discipline, with research experience in machine learning, deep learning, or biomedical image analysis.

Key Responsibilities:

  • Design, develop, and train convolutional neural networks (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.
  • 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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