IITGN JRF Recruitment 2026

IITGN JRF Recruitment 2026

Indian Institute of Technology Gandhinagar (IIT Gandhinagar) - Department of Computer Science & Engineering

Vacancy

1

Salary

₹37,000 - ₹37,000

Job Type

contract

full time

Apply By

19 May 2026

About This Job

Apply for 1 JRF post at IITGN. PG Basic Science or Graduate/PG Professional degree with NET/GATE eligible. Salary: Rs 37,000. Last date: 19 May 2026.

About This Job

IIT Gandhinagar is hiring 1 Junior Research Fellow (JRF) in the Department of Computer Science & Engineering. If you have a Post Graduate Degree in Basic Science or a Graduate/Post Graduate Degree in a Professional Course with NET/GATE or another approved national-level selection route, you can apply online. The last date to apply is 19 May 2026. The consolidated monthly pay is Rs. 37,000.

IIT Gandhinagar JRF Recruitment 2026 - 1 Post

Indian Institute of Technology Gandhinagar (IITGN) has issued Advertisement No. SPON/70143/Advt0194 dated 05 May 2026 for a temporary JRF position under Prof. Nipun Batra. The project is titled Automated Crop mapping using time-series satellite and ancillary data. This is a 12-month research appointment linked to an ISRO-IIT Gandhinagar research effort on AI for agricultural intelligence.

Who Can Apply?

  • Essential qualification: Post Graduate Degree in Basic Science, or Graduate/Post Graduate Degree in a Professional Course.
  • Selection route: You should be selected through CSIR-UGC NET, GATE, or a national-level examination conducted by agencies/institutions such as DST, DBT, DAE, DOS, DRDO, MoE, ICAR, ICMR, IIT, IISc, IISER, NISER and similar bodies.
  • Department: Computer Science & Engineering.
  • Project area: AI/ML for crop mapping using satellite imagery and supporting weather, soil and crop-calendar data.

Desirable Skills

  • Comfort with Python and ML libraries such as PyTorch, scikit-learn, NumPy and pandas.
  • Experience with at least one end-to-end ML project through coursework, internship, hackathon or open-source work.
  • Familiarity with Git/GitHub and reproducible coding practices.
  • Interest in remote sensing, satellite imagery, geospatial data, agriculture, climate or sustainability.
  • Ability to read and write research papers or technical documents.

Salary

  • Consolidated monthly pay: Rs. 37,000 per month.
  • Appointment duration: 12 months.
  • This is a temporary project position at IIT Gandhinagar.

Selection Process

  1. Application Screening: IITGN will review online applications for eligibility, national test qualification and project fit.
  2. Shortlisting: Candidates with suitable ML, coding, geospatial or research experience may be shortlisted.
  3. Interview: Shortlisted candidates are expected to appear for the selection interaction as informed by IITGN.

What You Will Work On

  • Build machine-learning and deep-learning models for in-season field-level crop maps.
  • Work with time-series satellite data from sources such as Sentinel-1/2, Landsat and other inputs.
  • Use multi-modal data fusion across optical, SAR, weather, soil and topographic data.
  • Study self-supervised, semi-supervised and weakly-supervised learning on large satellite archives.
  • Prepare reproducible experiments, open-source tooling and research papers for ML or remote-sensing venues.

How to Apply

  1. Go to the IIT Gandhinagar project staff recruitment portal at recruitment.iitgn.ac.in/projectstaff.
  2. Register or log in on the project staff portal.
  3. Select the JRF opening under Computer Science & Engineering with Prof. Nipun Batra.
  4. Fill in your academic, national test, project and experience details.
  5. Submit the online form before 19 May 2026.

Important Dates

  • Advertisement Date: 05 May 2026
  • Application Start Date: 05 May 2026
  • Last Date to Apply: 19 May 2026

Responsibilities

  • Develop ML and deep-learning models for crop mapping from time-series satellite imagery.
  • Work with optical, SAR, weather, soil, crop-calendar and topographic datasets.
  • Design reproducible Python experiments using ML libraries and clean version-control practices.
  • Support model generalisation across geographies, seasons and sensors.
  • Help prepare technical reports, research papers and open-source tools for the project.

Eligibility Criteria

Minimum Qualification

Post Graduate Degree in Basic Science OR Graduate/Post Graduate Degree in Professional Course with CSIR-UGC NET/GATE or another approved national-level examination route

Preferred Qualification

Python and ML libraries, end-to-end ML project experience, Git/GitHub, interest in remote sensing, satellite imagery, agriculture, climate or sustainability

Required Skills

Python, PyTorch/scikit-learn/NumPy/pandas, machine learning, deep learning, Git/GitHub, research writing, remote sensing interest

Salary & Compensation

Pay Scale

Consolidated Rs. 37,000 per month

Allowances

Consolidated monthly remuneration as per advertisement

Selection Process

  1. 1 Interview

Exam Pattern

There is no written exam mentioned in the notification. Selection is expected through application screening and interview/interaction for shortlisted candidates.

Additional Requirements

Computer Knowledge Required

How to Apply

Application Mode: online

Click the "Apply Now" button to visit the official application portal.

Required Documents:

  • Educational qualification certificates
  • NET/GATE or other national-level examination proof
  • Resume/CV
  • Experience or project proof, if available
  • Identity proof
  • Category certificate, if applicable

Frequently Asked Questions

The last date to submit the online application is 19 May 2026.

The selected JRF will get a consolidated monthly pay of Rs. 37,000.

You need a Post Graduate Degree in Basic Science or a Graduate/Post Graduate Degree in a Professional Course with CSIR-UGC NET, GATE or another approved national-level selection route.

Apply online through the IITGN project staff portal at recruitment.iitgn.ac.in/projectstaff before the deadline.

The project focuses on automated crop mapping using time-series satellite and ancillary data with machine learning and deep learning.

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