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3-Month Hybrid Fellowship
Final Year B. Tech Students
Closed
Date: 23 rd March 2026
About the Fellowship Programme
The NAMTECH Fellowship Programme in Intelligent Robotics and AI Systems is a structured, three-phase academic and project-oriented programme designed to provide
strong foundations and applied exposure in robotics, artificial intelligence, and autonomous systems.
Online Component:
Conceptual learning and expert-led sessions delivered through virtual platforms focusing on simulation and coding.
On-Campus Component:
Hands-on project implementation, experimentation, and system integration
Why This Internship is Different?
Not Just Another Internship. A Launchpad into Intelligent Robotics Careers.
Strong Foundation in AI for Robotics
Build core knowledge of AI-driven perception, decision-making, and control in robotics.
Work with Industrial Robots & Cobots
Gain hands-on experience operating and programming industrial robots and cobots.
Mentorship by Robotics Experts
Learn directly from industry professionals with practical guidance and insights.
Python-based Simulations (SLAM, Kalman Filter, PID, Particle Filtering)
Develop and test key robotics algorithms using Python-based simulations.
Real Project Implementation on Campus
Work on live projects to apply concepts in real-world scenarios.
Exposure to Industry Case Studies
Explore real applications of robotics through curated industry case studies.
Programme Overview
Phase 
Programme Benefits 
Eligibility Eligibility 
Shortlisting Criteria Phase 1: April 2026 (Online)
AI/ML for Robotics – Fundamentals focusing on simulations and coding. Activities include registration, webinars, and structured assignments.
| MODULE & WHAT YOU EXPLORE | ASSESSMENT |
|---|---|
| FoundationsScope of AI/ML in Robotics; Intelligent & Rational Agents. | ASSESSMENTMake a conceptual design of Wheeled Mobile robot and sensors to perform collision avoidance write a python script showing logic. |
| Agent TypesSimple reflex, model-based, goal-based, and utility agents. | ASSESSMENTWrite a Python script to simulate a mobile robot moving in grid space, avoiding collision with timid reaction. |
| Symbolic AIProblem-solving agents; state-space representation; search strategies. | ASSESSMENTWrite a Python script to simulate a mobile robot moving in grid space, avoiding collision until it reaches its goal using a heuristic path planning algorithm. |
| Logic & UncertaintyFirst-Order Logic; probability theory; Bayes’ theorem. | ASSESSMENTWrite a Python script to simulate a mobile robot moving in grid space, avoiding collision until it reaches its goal using a heuristic path planning algorithm, assigning random errors to the earlier code. |
| Planning & LocalizationClassical/heuristic planning; dead reckoning principles. | ASSESSMENTWrite a Python script to perform localisation based on the dead-reckoning principle. |
| Probabilistic EstimationBayesian framework; Kalman Filter (KF) formulation. | ASSESSMENTImplement Kalman filtering for noisy sensor data (given or random). Generate random sequence numbers, assign error %, and implement Kalman filtering in a Python script. |
| Advanced FilteringAlpha–Beta–Gamma filtering; linear system assumptions. | ASSESSMENTImplement mapping for a random set of data generated in Python and apply Alpha–Beta–Gamma filtering. |
| Control SystemsReactive vs predictive control; PID tuning and stability. | ASSESSMENTWrite a Python script to implement PID control for randomly generated localisation error and generate a PWM control signal. |
| Monte Carlo MethodsParticle filtering; importance sampling and resampling. | ASSESSMENTImplement particle filtering-based localisation in a Python simulation. |
| SLAMSimultaneous Localization and Mapping; mapping challenges. | ASSESSMENTImplement SLAM in a Python simulation. |
Phase 2: April 2026 (Online)
Expert seminars, industry talks, applied case studies, project floating, and guided group discussions for project formulation.
| Week 1 & 2Seminar | Week 3Project Floating |
| Week 4Group Discussion | |
Phase 3: May 2026 (On-Campus)
Full-time on-campus, project-based learning involving design, development, testing, and demonstration under faculty mentorship.
- AI-Based Pick-and-Place Robotic Arm
- Seismic Survey Robot for Underground Mining Applications
- Mine Safety Inspection Robot for Hazardous Environments
- Computer Vision-Based Structural Inspection Robot
- Mini Robot for ENT and Oral Inspection
- Physiotherapeutic Robot for Simulating Paralyzed Muscles
NAMTECH Fellowship Programme Benefits
- Opportunity to work on Industry-Related Projects.
- Funding Support per project for approved project-related expenses.
- Free access to the Phase-1 online AI/ML for Robotics course.
- Webinars, seminars, and industry talks conducted at the expense of NAMTECH.
- Exposure to real-world industry and research case studies.
- Academic mentoring and technical guidance throughout the programme.
- On-campus accommodation subject to availability (not mandatory) at the payment of actual fees.
- Fellowship Certificate on successful completion.
- Letter of Recommendation.
- Access to NAMTECH’s Industry grade world Class Robotics Lab established by
- ABB, Fanuc, and Addverb.
Eligibility
Currently pursuing B.Tech (4th Year, 8th Semester) in Mechanical, Production, Industrial, Manufacturing, Electrical, Electronics, or Computer Science Engineering from any recognized institution/university. Minimum 6.0 CGPA / 60% marks with no active backlogs.
Application Process
STEP 1
Submit online application via Microsoft Forms..
STEP 2
Upload scanned Student ID and latest semester marksheet.
STEP 3
Write a Statement of Interest (max 200 words)
STEP 4
List projects, research, papers, or internships completed to date, with your latest resume.
Shortlisting Criteria
Candidates will be shortlisted based on the following weightage:
50%
Academic Performance
30%
Projects / Research / Internships
20%
Statement of Interest
Faculty
Expert seminars, industry talks, applied case studies, project floating, and guided group discussions for project formulation.
