Senior Computational Biologist

5 days ago

Kurnool, Andhra Pradesh, India beBeeMachineLearning Full time ₹ 1,20,00,000 - ₹ 2,00,00,000

Job Title: Senior Computational Biologist

Location: Delhi

Experience: Freshers

Job Description:

We are seeking a highly motivated PhD candidate with expertise in computational biology and machine learning to join our team. The successful candidate will contribute to the development and optimization of deep learning models for cancer classification and spatial mapping using digitized H&E slides.

The ideal candidate will have a strong background in computer science, biomedical engineering, or a related discipline, with demonstrated research experience in machine learning, deep learning, or biomedical image analysis. Experience with digital pathology workflows, image preprocessing/augmentation, and annotation tools is also highly desirable.

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.

Qualifications:

  • 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:

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

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