BITS Pilani Research Fellow Economics Recruitment 2026

BITS Pilani Research Fellow Recruitment 2026: Overview

Birla Institute of Technology and Science, Pilani (BITS Pilani), Hyderabad Campus is hiring a Research Fellow for a sponsored project in its Department of Economics and Finance. The International Rice Research Institute (IRRI) funds the project under the CGIAR Gender Equality and Inclusion Accelerator. The appointment is a short project contract based at the Hyderabad campus.

The project is called Building the Business Case for Gender-Responsive Extension and Advisory Services. Its focus brings economics research into a study of gender-responsive services. You would join the project team led by Bheemeshwar Reddy A, Associate Professor, rather than apply to a general research-fellow pool across the institute.

Service Conditions and Caveats

• Project tenure: The appointment lasts two months. The notice describes a defined project engagement, so you should consider that short duration when planning your availability.
• Work location: The selected researcher will be stationed at BITS Pilani, Hyderabad Campus. Keep this campus requirement in mind when deciding whether the engagement suits you.
• Project setting: The fellowship belongs to the named IRRI-funded study within the Economics and Finance department. Use that project identity when contacting the team so your enquiry reaches the right recruitment context.

Application Submission Notes

Send your application by email to bheem@hyderabad.bits-pilani.ac.in. Use the subject line Application for RA - IRRI Project, exactly as the notice requests, even though the advertised position is called Research Fellow.

Email is the stated application route for this project. The campus named in the advertisement remains the place of work; do not treat an email-based application as an indication that the fellowship is a remote appointment.

• Analyse secondary data for the gender-responsive services project.
• Apply econometric methods to the project research.
• Handle and analyse large-scale survey datasets.
• Implement causal inference techniques within project deadlines.
• Use Python, R or Stata for empirical analysis.