Machine Learning Engineer - Spatial Omics

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

Experience: Freshers

Location: Delhi

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.