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.