Senior Data Scientist for Advanced Computational Methods

6 days ago

Lucknow, Uttar Pradesh, India beBeeMachineLearning Full time ₹ 15,00,000 - ₹ 25,00,000

Job Title: Senior Data Scientist for Advanced Computational Methods

We are seeking a highly motivated PhD candidate to lead the development and optimization of deep learning models for histopathology image analysis in a multidisciplinary research environment.

This role is well-suited for researchers aiming to apply advanced computational methods to biomedical challenges, such as cancer diagnosis and treatment.

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 to enhance model performance and efficiency.
  • 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 for improved accuracy and robustness.
  • Analyze model performance and contribute to improving accuracy, efficiency, and robustness through continuous monitoring and evaluation.
  • Document research findings and contribute to publications in peer-reviewed journals to disseminate knowledge and advancements in the field.

Required Skills and Qualifications:

  • PhD in Computer Science, Biomedical Engineering, Data Science, Computational Biology, or a related discipline with a strong focus on machine learning and deep learning.
  • Demonstrated research experience in machine learning, deep learning, or biomedical image analysis with a proven track record of innovative contributions.
  • Strong programming skills in Python with experience using popular machine learning frameworks such as TensorFlow or PyTorch.
  • Familiarity with digital pathology workflows, image preprocessing/augmentation, and annotation tools to ensure seamless integration and collaboration.
  • Ability to work collaboratively in a multidisciplinary research environment, communicating effectively with colleagues from diverse backgrounds and expertise.

Preferred Qualifications:

  • Background in cancer histopathology or biomedical image analysis with a strong understanding of spatial transcriptomics and its applications.

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