IIIT Delhi Research Assistant SBILab Recruitment 2026

IIIT Delhi Research Assistant Recruitment 2026: Overview

Indraprastha Institute of Information Technology Delhi (IIIT Delhi) is recruiting a Research Assistant at its Signal Processing and Biomedical Imaging Laboratory (SBILab), under Prof. Anubha Gupta. The appointment supports artificial intelligence and deep learning research for healthcare applications. SBILab studies biomedical signals, medical imaging and interpretable artificial intelligence, with an emphasis on systems that can be used in clinical and healthcare settings.

Service Conditions and Caveats

• Fixed project term: The appointment is for eight months. The notice describes it as temporary, so you should consider the stated project term when deciding whether this opportunity fits your plans.
• Probation: Continuing in the assignment depends on successfully completing a one-month probation period. This condition is part of the advertised engagement, rather than a separate long-term employment offer.
• Research supervision: You will report to Prof. Anubha Gupta within SBILab. The named laboratory and supervisor identify the research setting for this particular Research Assistant appointment.
• Applied research setting: The laboratory’s stated focus is on trustworthy, transparent and deployable artificial intelligence. Its healthcare emphasis connects model development with the wider aim of use in real clinical settings.
• Institute rules: IIIT Delhi’s hiring rules govern the appointment, and the institute retains the final decision on recruitment matters. The notice does not set out a separate promise of continued employment after the advertised term.
• Availability caveat: The institute’s project-position listing adds that recruitment may continue until suitable candidates are found. The direct notification nevertheless gives a fixed application deadline, which is retained in the date field for this listing.

• Capture data for machine learning models.
• Prepare and annotate datasets for the assigned healthcare tasks.
• Establish performance baselines using custom deep learning models.
• Develop front-end tools for deploying the models.
• Build back-end components needed for deployment.