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PHD in Artificial Intelligence And Robotics at Indian Institute of Technology Mandi
Mandi, Himachal Pradesh
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About the Specialization
What is Artificial Intelligence And Robotics at Indian Institute of Technology Mandi Mandi?
This Artificial Intelligence and Robotics program at Indian Institute of Technology Mandi, while typically structured with flexible coursework chosen from advanced M.Tech/PhD level courses, focuses on cutting-edge research at the intersection of intelligent systems and autonomous machines. Given India''''s burgeoning tech sector and increasing adoption of automation across manufacturing, healthcare, and logistics, this specialization is designed to cultivate researchers capable of driving innovation in these critical fields. The program leverages IIT Mandi''''s strong faculty expertise in machine learning, computer vision, natural language processing, and advanced robotics, offering a unique blend of theoretical depth and practical application relevant to the evolving Indian industrial landscape.
Who Should Apply?
This program is ideal for highly motivated individuals holding a Master''''s degree in Computer Science, Electrical Engineering, or a related discipline, possessing a strong academic record and a keen interest in fundamental and applied research. It also welcomes exceptional B.Tech/B.E. graduates with a high CPI who demonstrate significant research aptitude. Working professionals from R&D divisions in Indian tech companies seeking to advance their expertise or transition into research-oriented roles will find the program''''s flexible coursework beneficial for upskilling. Prerequisite backgrounds typically include solid foundations in mathematics, programming, data structures, and algorithms, alongside a basic understanding of AI or control systems.
Why Choose This Course?
Graduates of this program can expect to pursue impactful careers as research scientists, AI/Robotics engineers, or academicians within India and globally. Opportunities abound in India''''s leading R&D labs of MNCs like Samsung, Intel, and Google, as well as in pioneering Indian startups focusing on automation, autonomous vehicles, and intelligent manufacturing. Starting salaries for PhD graduates in AI/Robotics in India typically range from INR 15-30 LPA, with significant growth trajectories for experienced professionals and those moving into leadership roles. The rigorous research training also prepares scholars for post-doctoral positions and faculty roles in premier Indian academic institutions, contributing to the nation''''s scientific advancement.
Student Success Practices
Foundation Stage
Build a Strong Theoretical Foundation in AI/ML- (Initial 1-2 years of PhD)
Dedicate initial semesters to mastering core AI/ML concepts by diligently taking advanced coursework (e.g., Deep Learning, Reinforcement Learning). Focus on understanding the underlying mathematics and algorithms, not just implementation. Actively participate in class discussions and solve challenging theoretical problems.
Tools & Resources
NPTEL courses on AI/ML, Deep Learning by Goodfellow et al., Pattern Recognition and Machine Learning by Bishop, Peer study groups, IIT Mandi''''s central library resources
Career Connection
A solid theoretical base is critical for designing novel algorithms and solving complex research problems, highly valued in top-tier research positions and academic roles.
Engage Early with Research Publications- (From Semester 1 onwards)
Beyond coursework, start reading seminal and recent research papers in AI and Robotics from top conferences (NeurIPS, ICML, CVPR, ICCV, ICLR, RSS, ICRA). Join a research lab''''s weekly paper reading group or initiate one with peers. Summarize and critique papers to develop critical thinking and identify potential research gaps.
Tools & Resources
ArXiv, Google Scholar, IEEE Xplore, ACM Digital Library, Zotero/Mendeley for reference management, Internal department seminars
Career Connection
Early exposure to state-of-the-art research helps in identifying a focused PhD topic and developing presentation skills essential for conferences and future employment.
Develop Robust Programming and Experimentation Skills- (Initial 1-2 years of PhD)
Translate theoretical knowledge into practical skills by actively participating in coding assignments for advanced courses and starting small personal projects. Become proficient in Python, PyTorch/TensorFlow, and relevant Robotics simulation platforms (e.g., ROS, Gazebo). Document code effectively and learn version control.
Tools & Resources
GitHub, Kaggle competitions, Google Colab/Jupyter Notebooks, IIT Mandi''''s computing facilities, Online coding tutorials (e.g., freeCodeCamp, Coursera specializations)
Career Connection
Strong implementation skills are crucial for conducting experiments, validating hypotheses, and are highly sought after by R&D teams in industry.
Intermediate Stage
Actively Collaborate with Faculty and Peer Researchers- (Semesters 3-5)
Proactively seek out faculty working in your areas of interest for discussions, potential mentorship, and research project involvement. Collaborate with senior PhD students on their ongoing projects to learn research methodologies and gain practical experience. Attend internal research presentations and give feedback.
Tools & Resources
Departmental research groups, Faculty office hours, Inter-departmental workshops, IIT Mandi''''s research portal
Career Connection
Collaboration fosters networking, leads to co-authored publications, and provides exposure to diverse research approaches, critical for building a strong research profile.
Aim for High-Quality Publications and Conference Participation- (Semesters 3-5)
Focus on producing publishable research findings. Target top-tier international conferences and journals relevant to AI and Robotics. Prepare well-structured papers and posters. Attend at least one major conference (e.g., ICRA, IROS, AAAI, IJCAI) to network, present work, and stay updated.
Tools & Resources
LaTeX, Overleaf, Institutional grants for conference travel, Mentorship from supervisor for paper writing and submission
Career Connection
Publications in prestigious venues are a primary metric for academic success and significantly boost desirability for research roles in both academia and industry.
Seek Relevant Internships or Research Sabbaticals- (During semester breaks or designated research periods in Semesters 4-6)
Explore opportunities for research internships at leading AI/Robotics companies (e.g., TCS Research, DRDO, robotics startups in Bangalore/Hyderabad) or academic labs abroad. This provides industry exposure, helps in validating research ideas, and builds a professional network beyond academia.
Tools & Resources
IIT Mandi''''s Career Development & Placement Cell, Faculty network, LinkedIn, Specialized research internship portals
Career Connection
Internships offer practical experience, often lead to pre-placement offers, and clarify career paths in R&D, bridging the gap between academic research and industry application.
Advanced Stage
Systematize Thesis Writing and Defense Preparation- (Semesters 6-8)
Develop a detailed thesis outline early on and regularly update it. Dedicate consistent blocks of time to writing, even for preliminary chapters. Practice mock thesis defenses with peers and faculty to refine presentation and anticipate questions.
Tools & Resources
Mendeley/Zotero, Grammarly, Academic writing workshops, Feedback sessions with supervisor and Doctoral Scrutiny Committee (DSC)
Career Connection
A well-written thesis and a confident defense are essential for graduating and demonstrating the culmination of your research capabilities to potential employers or academic institutions.
Build a Professional Online Presence and Network Strategically- (Semesters 6-8)
Maintain an up-to-date academic website/portfolio showcasing your research, publications, and projects. Actively use LinkedIn to connect with researchers and industry professionals. Attend job fairs, alumni events, and industry seminars to explore opportunities.
Tools & Resources
Personal website (GitHub Pages, Google Sites), LinkedIn, ResearchGate, Departmental alumni networks
Career Connection
A strong online presence and professional network are invaluable for job searching, identifying postdoctoral positions, and securing referrals in the competitive AI and Robotics landscape.
Develop Mentorship and Leadership Skills- (Semesters 6-8)
Mentor junior PhD or M.Tech students in the lab, assisting them with research challenges or project work. Take on leadership roles in student chapters or departmental initiatives. This cultivates leadership, communication, and project management skills.
Tools & Resources
Departmental student organizations, Lab group meetings, Opportunities to co-supervise B.Tech/M.Tech projects
Career Connection
These skills are highly valued in both academic leadership roles and industry positions that require leading R&D teams, project management, and cross-functional collaboration.
Program Structure and Curriculum
Eligibility:
- M.Tech./M.E. degree in a relevant branch with minimum CPI of 6.5 or 60% marks; OR B.Tech./B.E. degree in a relevant branch with minimum CPI of 7.5 or 70% marks; OR M.Sc./M.A. degree in a relevant branch with minimum CPI of 6.5 or 60% marks and a valid GATE/NET score or equivalent.
Duration: Minimum 3 years post M.Tech, 4 years post B.Tech
Credits: Minimum 16 credits for coursework Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester undefined
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| AI501 | Foundations of Artificial Intelligence | Core (for AI specialization) | 6 | Problem Solving Agents, Search Algorithms, Knowledge Representation, First-Order Logic, Planning, Uncertainty and Probabilistic Reasoning |
| AI502 | Machine Learning | Core | 6 | Supervised Learning, Unsupervised Learning, Model Evaluation and Validation, Regression and Classification Techniques, Clustering Algorithms, Dimensionality Reduction |
| AI503 | Deep Learning | Core | 6 | Neural Network Architectures, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transformers and Attention Mechanisms |
Semester undefined
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS601 | Robotics | Elective/Specialized Core | 6 | Robot Kinematics and Dynamics, Trajectory Generation, Robot Control Systems, Robot Vision and Sensing, Motion Planning and Navigation, Robot Learning |
| AI504 | Computer Vision | Elective/Core | 6 | Image Processing Fundamentals, Feature Detection and Description, Object Recognition and Tracking, Image Segmentation, 3D Computer Vision, Deep Learning for Vision |
| AI506 | Reinforcement Learning | Elective/Core | 6 | Markov Decision Processes (MDPs), Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning (Q-learning, SARSA), Policy Gradient Methods, Deep Reinforcement Learning |
