Postdoctoral Fellowship

Job Summary

Job description

This position is part of the "Machine Learning Health Informatics (ML-HI)" Research Group - a dynamic RD team focusing on intelligent, data-driven algorithms for real-world healthcare and decision-support systems. This position will involve RD focused on:

• Design and development of deep learning architectures (CNN, RNN/LSTM, Transformer-based models) for disease prediction, diagnosis, and severity estimation.
• Development and benchmarking of novel optimization algorithms (metaheuristic, bio-inspired, and hybrid optimization techniques) for improving model accuracy and efficiency.
• Application of machine learning and deep learning techniques to healthcare analytics, clinical/medical data mining, and predictive modelling.

The selected candidate will also have opportunities to collaborate with allied research centres at Amrita Chennai for RD projects focused on:

• Explainable AI (XAI) and trustworthy ML models for clinical decision support.
• Optimization-driven hyperparameter tuning and model compression for real-time healthcare applications.
• Data mining, NLP, and predictive analytics applied to biomedical and clinical datasets.

The position also offers the opportunity to participate in nationally and internationally funded collaborative RD projects in the areas of AI/ML, optimization, and healthcare informatics.

Responsibilities

• Conduct and lead high-impact research in machine learning, deep learning, and optimization algorithms for healthcare applications.
• Develop, validate, and benchmark predictive and diagnostic models against standard healthcare datasets.
• Supervise and guide undergraduate and doctoral researchers in the team.
• Collaborate with interdisciplinary teams, contribute to grant proposals, research publications, and industrial/clinical partnerships.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.