IIT Gandhinagar Post Doctoral Fellow Recruitment 2026: Eligibility, Salary, Last Date

IIT Gandhinagar Post Doctoral Fellow Recruitment 2026: Eligibility, Salary, Last Date

IIT Gandhinagar Post Doctoral Fellow Recruitment 2026: Overview

The Indian Institute of Technology Gandhinagar (IIT Gandhinagar) has released an official notification for the recruitment of a Post Doctoral Fellow. This position is available in the Department of Chemistry under the Computational Materials Discovery Group.

This is a research-oriented opportunity for candidates with a Ph.D. in relevant disciplines. The recruitment is strictly based on Direct Recruitment through application screening and interview; no competitive examination is involved. Selected candidates will work on a project titled "Data-Driven Inverse Design of Multifunctional TADF Materials for Optoelectronics, Photocatalysis, and Solar Thermal Storage."

Key Highlights Table

Vacancy Details

Eligibility Criteria

Educational Qualification

• Essential: Ph.D. Degree in Chemistry, Physics, Materials Science, Chemical Engineering, or related discipline from a recognized institution.
• Academic Performance:
  • Minimum 60% marks or equivalent CGPA from Graduation onwards.
  • Minimum 55% marks or equivalent grade in Class 10th and Class 12th.

Preferred Skills and Experience

Candidates with prior experience in the following areas are preferred:

• Computational Areas: Density Functional Theory (DFT), Ab initio Molecular Dynamics (AIMD), Classical Molecular Dynamics (MD), Kinetic Monte Carlo (KMC).
• Programming: Excellent abilities in Python, Fortran, or C++. Knowledge of high-performance computing and computational chemistry software.
• Desirable: High-throughput computing, Machine learning methods, Optimization methods, and strong analytical skills.

Age Limit

As per Institute norms. (Typically for Post Doctoral roles, there is no strict upper age limit, but candidates should verify specific age criteria in the official notification).

Selection Process

This is a Direct Recruitment (Merit-based). No written exam is conducted. The selection involves the following stages:

1. Application Screening: Checking for completeness and eligibility.
2. Evaluation: Assessment of research background, computational skills, academic qualifications, and publications.
3. Interview: Shortlisted candidates will be called for an interview with the research group.
4. Final Selection: Based on overall merit and interview performance.

Application Fee

Candidates are advised to check the official notification PDF for any fee requirements or exemptions.

How to Apply (Step-by-Step)

Interested candidates must follow these instructions to apply offline via email:

1. Prepare Documents: Gather all required documents listed below.
2. Combine PDF: Combine all documents into a single PDF file.
3. Send Email: Email the complete application to: [email protected]..
4. Subject Line: Use the specific subject: "Application for Post-Doctoral Fellow - TADF Materials".
5. Recommendation Letters: Ensure two letters of recommendation are sent directly by the recommenders to the same email address.
6. Deadline: Submit your application before 10.02.2026.

Required Documents

• Detailed CV: Including academic track record, fellowships, awards, list of publications (journal papers, conference proceedings), invited talks, poster presentations, and technical skills.
• Cover Letter: Explaining suitability for the position.
• Research Statement: Not more than 1 page describing research interests and goals.
• Ph.D. Certificate: Scanned copy. If pending, provide confirmation of thesis submission/expected date.
• Recommendation Letters: Two letters (must be sent directly by recommenders).

Important Dates

• Last Date for Application Submission: 10-02-2026
• Position Availability: Immediate

Important Links

• Official Notification PDF
• Official Website

Job Responsibilities

• Conduct computational research on TADF materials design.
• Perform Density Functional Theory (DFT) and Molecular Dynamics simulations.
• Develop and apply machine learning models for materials discovery.
• High-throughput screening of materials and data analysis.
• Contribute to research publications and presentations.
• Collaborate with researchers and mentor students.