Research Associate, 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

Work directly with the Project Scientist to build, optimize and run high-throughput bioimage processing pipelines and statistical workflows, bridging raw imaging and spatial datasets with multi-omics integration to power our digital tissue mapping work.

Key Responsibilities

• Standardize, QC and process whole-slide images, correcting for stain variability and artifacts
• Implement and optimize deep learning/ML algorithms for single-cell segmentation, phenotyping and nuclear morphology classification
• Develop spatial alignment algorithms to co-register multiplexed imaging with spatial transcriptomics and 3D histology maps
• Manage and analyze multi-omics and genomic pipelines with version control and reproducibility
• Process and integrate liquid biopsy sequencing metrics with tissue microenvironment datasets
• Prepare analytical reports and visualizations for clinicians, biologists and data scientists

Required Qualifications

• B.E/B.Tech in Computer Science, or Master's in Computational Biology, Bioinformatics, Data Science or a related field
• 2 years of hands-on experience in biological data analysis, Python/R scripting, image processing and genomics in cancer research
• Attention to detail, reproducibility and strong communication skills

Preferred

• Cloud computing experience (AWS/GCP)
• Peer-reviewed publications in computational biology, bioinformatics or oncology
• Familiarity with spatial transcriptomics platforms (Visium, CosMx) and scRNA-seq tools
• Open-source bioinformatics contributions

How to Apply:

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