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Junior Research Fellow (JRF)

Indian Institute of Technology, Guwahati

Guwahati, Assam, India · 정규직

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2주 전
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M.Tech
적임
M.Tech/ME graduates in Structural Engineering or Earthquake Engineering with GATE qualification and a strong interest in research are eligible to apply.
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Role Overview

Indian Institute of Technology Guwahati is inviting applications for a Junior Research Fellow (JRF) position under an ANRF-supported ARG project in the area of structural engineering and AI/ML. The work focuses on developing affordable seismic strengthening solutions and data-driven fragility assessment methods for reinforced concrete OGS frames.

Project Focus

The selected researcher will contribute to a multidisciplinary study that brings together structural mechanics, earthquake engineering, and machine learning. The project title is “Low-Cost Hybrid Yielding Dampers and ML-Based Fragility Modeling for Seismic Strengthening of Non-Ductile RC OGS Frames.”

Key Work Areas

The role involves hands-on research and analysis across experimental, analytical, and computational tasks. The candidate will support large-scale testing, design and development of hybrid yielding dampers, fragility and vulnerability modeling using machine learning, and advanced numerical simulation work.

Candidate Profile

Applicants should have an M.Tech or M.E. in Structural Engineering or Earthquake Engineering and must have qualified GATE. A genuine interest in research, problem-solving, and engineering innovation is important. Prior exposure to Python or other programming tools, as well as experience in experimental structural testing, will be an added advantage.

Important Dates

The application deadline is 22 July 2026 at 5:00 PM. The interview is scheduled for 27 July 2026 and will be conducted online.

Contact

For any project-related queries, candidates may contact [email protected].

Additional Information

This opportunity is suited for motivated candidates who want to work on next-generation seismic strengthening systems that combine structural engineering principles with AI-driven modeling approaches.

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