Artificial Intelligence Researcher

AI for Early Cancer Detection (Computer Vision)Location: On-site — Kochi, Kerala, IndiaEmployment: Full-time (6–12 month contract with potential extension)Start date: Immediate## About the projectWe are building a pilot AI system for early cancer detection using advanced computer vision on medical imaging. The goal is to deliver a research-grade prototype that detects and prioritizes clinically relevant findings and integrates with standardized reporting workflows, paving the way for a prospective clinical study.## Role overviewThe Junior Researcher will work on-site and implement an end-to-end pipeline: data curation, preprocessing/DICOM handling, anatomical segmentation, classical candidate generation, 3D deep learning–based detection, evaluation, and a lightweight viewer for reader-in-the-loop assessment. Close mentorship and clear milestones are provided.## Key responsibilities- Data curation- Ingest and organize de-identified imaging studies and associated labels; maintain manifests, provenance, and documentation.- Structure labels into clinically meaningful size/risk bins; handle noisy/incomplete annotations.- Algorithm development- Preprocess volumetric CT data (resampling, denoising, normalization; multi-position alignment where applicable).- Implement segmentation of relevant anatomy and classical candidate generation to reduce false positives.- Train a 3D CNN detector on candidate-centered patches with weak/semi-supervised targets; optimize sensitivity for clinically significant findings while controlling false positives per case.- Optional: add a characterization head (e.g., risk/biologic likelihood) where reliable ground truth exists.- Evaluation and reporting- Define robust patient-level splits; compute per-lesion sensitivity by size/risk, false positives per case, and per-case sensitivity.- Build simple visualization/overlays and generate standardized, clinically aligned summaries for internal review.- Document methods, code, and results; contribute to an internal white paper and potential abstract.## Must-have qualifications- B.E./B.Tech/M.Sc./M.Tech in Computer Science, Biomedical Engineering, Data Science, or related fields.- Hands-on experience with Python, PyTorch/TensorFlow, and medical imaging toolkits (e.g., MONAI, SimpleITK, pydicom).- Practical knowledge of 3D CNNs/UNet variants, volumetric data pipelines, and GPU training workflows.- Demonstrated project in medical imaging or volumetric detection/segmentation (GitHub/portfolio or paper).- Strong experimentation hygiene: Git, reproducible environments, and clear documentation.- Willingness and ability to work on-site in Kochi, Kerala.## Nice-to-have- Experience with CT imaging, anatomical segmentation, or classical computer vision (e.g., curvature-based candidate generation, artifact suppression).- Familiarity with radiomics and weak/semi-supervised learning for noisy labels.- Understanding of clinical reporting frameworks and screening metrics.- Experience building simple viewers (Streamlit/Gradio/ITK widgets) and robust DICOM handling.- Exposure to data governance for clinical datasets and basic biostatistics.## What success looks like in 12 weeks- Curated and versioned imaging subsets ready for training, with label confidence tracking.- Baseline candidate generator with segmentation and measurable false-positive reduction.- 3D detection model achieving high sensitivity for clinically significant findings with controlled false positives per case.- Clear evaluation on a held-out test set and a lightweight reader-in-the-loop demo.## Tools and stack- Python, PyTorch, MONAI, SimpleITK/pydicom, NumPy/Pandas- Experiment tracking (Weights & Biases/MLflow), Docker/conda, Git- Optional: Streamlit/Gradio for viewer, DICOMweb utilities## What we offer- Exposure to translational research with potential publication/abstract opportunities.- Collaborative lab environment, competitive stipend, and performance-based extension.- Opportunity to impact real-world healthcare workflows.## How to applyEmail a brief note, CV, and links to relevant projects (GitHub/portfolio/papers) with subject "Junior Researcher – On-site (Kochi) – AI for Early Cancer Detection" to :- A short paragraph on experience with 3D medical imaging.- One example of handling noisy or incomplete labels.- Availability and preferred start date.- Confirmation of on-site availability in Kochi.## Application deadlineRolling review; priority for applications received within 2 weeks.Note: Prior domain-specific experience is a plus but not mandatory—strong fundamentals, curiosity, and grit matter most.