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DOCTOR-OF-PHILOSOPHY in Artificial Intelligence And Machine Learning at Bapuji Institute of Engineering & Technology
Davangere, Karnataka
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About the Specialization
What is Artificial Intelligence and Machine Learning at Bapuji Institute of Engineering & Technology Davangere?
This Artificial Intelligence and Machine Learning Ph.D. program at Bapuji Institute of Engineering and Technology, Davangere, focuses on cutting-edge research in areas driving India''''s digital transformation. It aims to develop highly skilled researchers capable of contributing to advancements in AI/ML, addressing complex challenges across various Indian industries such as healthcare, finance, and agriculture, while fostering innovation relevant to national priorities.
Who Should Apply?
This program is ideal for postgraduate degree holders in Computer Science, Information Science, or related engineering disciplines seeking to delve deeper into theoretical and applied aspects of AI/ML. It caters to aspiring academics, industry researchers, and innovators who wish to contribute original knowledge and develop solutions for critical problems in the rapidly evolving Indian tech landscape, building upon their master''''s level expertise.
Why Choose This Course?
Graduates of this program can expect to pursue careers as lead AI scientists, research engineers, data scientists, or faculty members in prominent Indian universities and R&D divisions of MNCs and startups. They will contribute to high-impact projects, potentially commanding competitive salaries (ranging from INR 15-30+ LPA for experienced researchers). The program prepares scholars for leadership roles in India''''s burgeoning AI sector and global research communities.
Student Success Practices
Foundation Stage
Master Research Methodology and Scientific Writing- (Semester 1-2)
Thoroughly grasp the principles of research design, data analysis, and ethical considerations. Focus on structuring research questions, conducting comprehensive literature reviews, and developing strong scientific writing skills essential for thesis and paper publications. Engage in departmental workshops on LaTeX and research tools.
Tools & Resources
Mendeley/Zotero for referencing, LaTeX for document preparation, Plagiarism check tools (e.g., Turnitin), VTU Central Library resources
Career Connection
Strong research methodology is fundamental for conducting credible research, securing grants, and publishing in top-tier conferences/journals, crucial for academic and industrial research careers.
Build a Robust AI/ML Foundational Knowledge- (Semester 1-2)
Even if coursework is general, independently review and solidify advanced concepts in machine learning, deep learning, natural language processing, and computer vision relevant to your proposed research. Participate in online advanced courses and MOOCs from platforms like Coursera/edX focused on AI/ML specializations.
Tools & Resources
Coursera/edX (Andrew Ng''''s ML, Deep Learning Specialization), Fast.ai, Kaggle for practical problem-solving, PyTorch/TensorFlow documentation
Career Connection
A deep theoretical and practical understanding of AI/ML fundamentals is essential to innovate, solve complex problems, and establish expertise in the chosen research domain, paving the way for specialized R&D roles.
Engage Actively with Research Community- (Semester 1-2)
Regularly attend department research seminars, join relevant Special Interest Groups (SIGs) within the college or VTU. Present preliminary literature review findings to peers and faculty to gain early feedback and establish networking connections within your research area. Seek out a strong mentor.
Tools & Resources
Departmental seminar series, IEEE/ACM student chapters, Researchgate/Academia.edu profiles
Career Connection
Early engagement builds a strong academic network, helps identify potential collaborators, and refines research direction, critical for future academic positions or collaborative industrial research.
Intermediate Stage
Deep Dive into Specialization through Targeted Research- (Semester 3-5)
Identify specific sub-areas within AI/ML for your doctoral thesis. Focus on solving a novel problem, starting with clear hypothesis formulation and experimental design. Utilize advanced software and hardware resources available at BIET or through collaborations.
Tools & Resources
GPU clusters (if available), Advanced Python libraries (SciPy, Scikit-learn, Hugging Face), Cloud platforms (AWS/Azure/GCP for compute)
Career Connection
Developing a focused research problem and demonstrating novel contributions is paramount for a successful Ph.D., leading to expertise in a niche and high demand in specialized AI/ML roles.
Target High-Impact Publications and Conferences- (Semester 3-5)
Aim to publish research findings in peer-reviewed journals (Scopus/Web of Science indexed) and present at reputed international/national conferences in AI/ML. Focus on understanding the publication process, peer review, and refining presentation skills for global audiences.
Tools & Resources
IEEE Xplore, ACM Digital Library, ArXiv for staying updated, Grammarly/QuillBot for manuscript refinement
Career Connection
Publications are a key metric for Ph.D. success, enhancing visibility, opening doors for post-doctoral fellowships, and demonstrating research acumen to potential employers in India and abroad.
Collaborate and Seek Interdisciplinary Projects- (Semester 3-5)
Actively seek opportunities to collaborate with other research scholars or faculty members, potentially across different departments (e.g., Electronics, Biotechnology) or institutions. Interdisciplinary projects often lead to innovative solutions and broader impact, especially for AI applications in diverse fields.
Tools & Resources
Departmental research groups, Industry hackathons/challenges, Collaborative research platforms
Career Connection
Collaborative experience fosters teamwork, expands problem-solving perspectives, and creates a wider professional network, beneficial for leadership roles and project management in R&D.
Advanced Stage
Refine Thesis and Prepare for Defense- (Semester 6-8 (or final year))
Dedicate significant time to writing, structuring, and refining your doctoral thesis. Ensure clarity, coherence, and originality. Practice defense presentations rigorously with faculty and peers, anticipating potential questions and feedback. Focus on highlighting your unique contributions.
Tools & Resources
Thesis writing guidelines (VTU), Grammar and style checkers, Presentation software (PowerPoint/Keynote)
Career Connection
A well-written and defended thesis is the culmination of your doctoral journey, directly impacting the quality of your Ph.D. and readiness for advanced research or academic positions.
Explore Post-Doctoral and Career Opportunities- (Semester 6-8 (or final year))
Proactively research post-doctoral positions in leading institutions (IITs, IISc, foreign universities) or R&D roles in major Indian tech companies and global MNCs. Tailor your CV and cover letter to specific roles, highlighting research impact and skill sets in AI/ML. Network with senior professionals.
Tools & Resources
LinkedIn, Naukri, Glassdoor for job searches, Academic job portals, University career services
Career Connection
Strategic career planning and active job searching in the final stage ensure a smooth transition into high-level research or academic roles post-Ph.D., capitalizing on the strong demand for AI/ML expertise.
Mentor Junior Researchers and Build Leadership Skills- (Semester 6-8 (or final year))
Take on mentorship roles for M.Tech/B.Tech students working on AI/ML projects. Lead departmental initiatives, contribute to lab management, and participate in organizing research events. This enhances leadership, communication, and teaching skills.
Tools & Resources
Departmental project groups, Research lab meetings, Teaching assistant opportunities
Career Connection
Mentoring and leadership experience are highly valued in both academia and industry, demonstrating readiness for senior research positions, project leadership, and faculty roles.
Program Structure and Curriculum
Eligibility:
- Master’s Degree in Engineering / Technology with a minimum of 6.75 CGPA (equivalent to 60% aggregate marks) OR Master’s Degree in Science / Humanities / Social Sciences / Law / Commerce with a minimum of 6.25 CGPA (equivalent to 55% aggregate marks) from UGC recognized Universities/AICTE approved Institutions. Candidates must also clear an Entrance Test (VTU-ETR or UGC-NET/CSIR-NET/GATE/KSET).
Duration: Minimum 3 years (Full-Time), Minimum 4 years (Part-Time)
Credits: Minimum 16, Maximum 24 (for coursework, as per VTU R22) Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22PHCRMI | Research Methodology and Intellectual Property Rights | Core | 4 | Introduction to Research and Research Design, Data Collection, Analysis and Interpretation, Research Report Writing and Presentation, Ethics in Research and Plagiarism, Intellectual Property Rights (Patents, Copyrights, Trademarks), Patent Filing Procedures and Case Studies |
| 22PHCSE | Advanced Course in Artificial Intelligence and Machine Learning | Core | 4 | |
| 22PHLSTP | Literature Survey and Presentation | Core | 4 |
