Job

• Ph.D. in Computer Science, Artificial Intelligence, Data Science, or a closely related discipline, with demonstrated expertise in Machine Learning, Deep Learning, or Optimization Algorithms. 


• Prior research experience in healthcare/biomedical applications of AI (e.g., disease prediction, medical image analysis, clinical decision support) will be an added advantage.

This position is part of the "Machine Learning & Health Informatics (ML-HI)" Research Group — a dynamic R&D team focusing on intelligent, data-driven algorithms for real-world healthcare and decision-support systems. This position will involve R&D 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 R&D 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 R&D projects in the areas of AI/ML, optimization, and healthcare informatics.

• 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.