IITK Research Associate II Chemistry Recruitment 2026

IITK Research Associate-II Recruitment 2026: Overview

Indian Institute of Technology Kanpur (IITK) is hiring for a contractual Research Associate-II appointment in its Department of Chemistry, with Dr. Nisanth N. Nair leading the project. The work belongs to SPAARQ: Software Platforms and Algorithms to accelerate Research in Quantum – HPC, a project sponsored by the Department of Science and Technology (DST).

Its focus is on battery materials, using density functional theory, molecular dynamics and machine-learned interatomic potentials to study condensed matter systems. You would join a computational research project that brings together molecular modelling and programming, with these methods applied to the materials being studied.

Service Conditions and Caveats

• Project appointment: The institute describes this position as temporary and contractual. Your appointment is linked to the named SPAARQ research project within the Chemistry department.
• Initial tenure: The appointment is for one year. This is the initial period offered through the advertisement, so you should use that period when considering the commitment involved.
• Extension: IITK may extend your appointment based on satisfactory performance. An extension is conditional, and the notice does not promise a fixed total period beyond the initial appointment.
• Project contact: The notice identifies Dr. Nisanth N. Nair in the Department of Chemistry, IIT Kanpur, Kanpur 208016. The named investigator and department help you identify the research group connected with this particular opening.
• Application route: The advertisement provides a Google Form and a QR code as routes to the application. Use the form attached to this specific role so that your application reaches the recruitment described in this notice.

• Study battery materials through computational modelling.
• Model condensed matter systems using density functional theory.
• Use molecular dynamics to study material behaviour.
• Work with machine-learned interatomic potentials.
• Apply programming skills to the project’s molecular modelling work.