Machine Learning Engineer - H&E Staining

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

Job Description:
We are seeking a motivated PhD candidate interested in machine learning for histopathology
image analysis.

The 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 apply advanced computational methods to
biomedical challenges.

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