Computational Biologist for Advanced Imaging Analysis

4 days ago

Ghaziabad, Uttar Pradesh, India beBeeMachineLearning Full time ₹ 10,00,000 - ₹ 20,00,000

Advanced Computational Biologist

We are seeking a highly motivated PhD candidate interested in applying machine learning to histopathology image analysis.

The ideal candidate will contribute to developing and optimizing deep learning models to analyze digitized H&E slides for cancer classification and spatial mapping. This role is well-suited for researchers aiming to leverage advanced computational methods to address biomedical challenges.

Key Responsibilities:

  • Design, develop, and train convolutional neural networks (CNNs) and related machine learning 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.

Qualifications:

  • PhD in Computer Science, Biomedical Engineering, Data Science, Computational Biology, or a related field.
  • 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.

This is an excellent opportunity for PhD candidates looking to apply advanced computational methods to address complex biomedical challenges.

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