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, IndiaWorkplace: On-siteType: Full-timeRole 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 ResponsibilitiesLead the computational pipeline for whole-slide image (WSI) processing, QC, registration and 3D tissue volume reconstructionDesign, implement and validate computer vision and deep learning models for cell segmentation, tissue phenotyping and spatial feature extractionIntegrate spatial histopathology features with spatial transcriptomics, genomic mutations, longitudinal clinical data, liquid biopsy dynamics and peripheral biomarkersBuild scalable, reproducible, containerized workflows (Python, R, Docker, Nextflow) for HPC and cloud environmentsWork with clinicians, pathologists, wet-lab scientists and bioinformaticians to turn findings into biological hypothesesRequired QualificationsPh.D. in Computational Biology, Bioinformatics, Data Science or a related discipline, with a focus on solid tumor histopathology and molecular genomic data analysisMinimum 2 years of post-doctoral or industry experience in end-to-end data analysis, AI/ML development and deploying computer vision workflows for digital pathologyExpertise 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 HPCPreferredExperience combining histopathology and spatial omics with liquid biopsy dynamics and longitudinal clinical datasetsDeep learning for automated WSI feature extraction and 3D tissue reconstructionScalable pipeline development and use of open-source pathology toolsHow to Apply:Apply using the Google Form link https://forms.gle/K6AHAnbKveDUPZrF8 and upload your CV.