Project Scientist, Computational Biology

About the Centre:

The Centre for 3D Cancer Omics at IIT Madras is a Focused Research Organization dedicated to advancing multidisciplinary digital tissue mapping and high-resolution histopathology in oncology. Operating at the intersection of clinical research, tissue imaging, molecular biology and computational science, we decode complex tumor microenvironments at scale. Our infrastructure integrates 3D histology, multiomics, spatial biology and liquid biopsy pipelines, with a focus on the unique genomic and clinical landscapes of Indian cohorts.

Company: Centre for 3D Cancer Omics

Location: IIT Madras Campus, Chennai, India

Workplace: On-site

Type: Full-time

Role Summary

Lead the development of computational architecture for digital pathology, multi-omics integration and spatial tissue modeling, turning complex tumor microenvironment data into actionable insights for precision oncology.

Key Responsibilities

• Lead the computational pipeline for whole-slide image (WSI) processing, QC, registration and 3D tissue volume reconstruction
• Design, implement and validate computer vision and deep learning models for cell segmentation, tissue phenotyping and spatial feature extraction
• Integrate spatial histopathology features with spatial transcriptomics, genomic mutations, longitudinal clinical data, liquid biopsy dynamics and peripheral biomarkers
• Build scalable, reproducible, containerized workflows (Python, R, Docker, Nextflow) for HPC and cloud environments
• Work with clinicians, pathologists, wet-lab scientists and bioinformaticians to turn findings into biological hypotheses

Required Qualifications

• Ph.D. in Computational Biology, Bioinformatics, Data Science or a related discipline, with a focus on solid tumor histopathology and molecular genomic data analysis
• Minimum 2 years of post-doctoral or industry experience in end-to-end data analysis, AI/ML development and deploying computer vision workflows for digital pathology
• Expertise in multi-modal data integration (spatial histopathology, single-cell/spatial transcriptomics, genomic variants, clinical metadata)
• Proficiency in PyTorch/TensorFlow, QuPath, OpenSlide, MONAI, and Docker/Nextflow on HPC

Preferred

• Experience combining histopathology and spatial omics with liquid biopsy dynamics and longitudinal clinical datasets
• Deep learning for automated WSI feature extraction and 3D tissue reconstruction
• Scalable pipeline development and use of open-source pathology tools

How to Apply:

Apply using the Google Form link https://forms.gle/K6AHAnbKveDUPZrF8 and upload your CV.