Fully Funded PhD in Efficient Machine Learning, Neuromorphic AI, and AI for Healthcare at Indiana University Indianapolis Indiana University Indianapolis in United States

Indiana University Indianapolis, Luddy School of Informatics, Computing, and Engineering is recruiting fully funded PhD students for Fall 2026 in the Department of Computer Science. The lab, led by incoming Assistant Professor Sayeed Chowdhury, focuses on cutting-edge research in energy-efficient brain-inspired machine learning (spiking neural networks, ANN–SNN hybrids), embodied and multimodal neuromorphic AI for agents and robotics, and AI-driven predictive healthcare using multimodal data (imaging, surgical videos, time-series, EHR/text). Students will have the opportunity to help build a new, collaborative lab from the ground up, with close mentoring and publication-focused projects.

Research directions include designing ultra-low latency neuro-inspired architectures, building multimodal systems for robotics, and developing predictive models for clinical decision-making. Collaboration with clinicians and researchers across IU and IU School of Medicine is encouraged.

Applicants should have a Bachelor’s or Master’s degree in Computer Science, Electrical and Computer Engineering, or related fields, with strong deep learning programming skills (PyTorch preferred) and interest in drones/embedded systems. Preferred qualifications include hands-on experience with drones/robots, camera/embedded system setup, prior experience with SNNs or medical data, and grant writing. CGPA flexibility is allowed if expertise is demonstrated.

Funding is provided via graduate assistantships, covering tuition and stipend. The priority deadline for applications is January 15, 2026. To apply, send a PDF with your CV, research statement, and optional portfolio links to [email protected]. Shortlisted candidates will be invited for a Zoom meeting and then encouraged to submit a formal application to the PhD program.

Key academic keywords: Machine Learning, Neuromorphic AI, Spiking Neural Networks, Computer Vision, Robotics, Healthcare AI, Deep Learning, PyTorch, Embedded Systems.