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PHD-SIT-PUNE in Artificial Intelligence And Machine Learning at Symbiosis International University (SIU)
Pune, Maharashtra
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About the Specialization
What is Artificial Intelligence and Machine Learning at Symbiosis International University (SIU) Pune?
This Artificial Intelligence and Machine Learning PhD program at Symbiosis International University, through SIT Pune, focuses on cutting-edge research and innovation. Given India''''s rapid digital transformation, AI/ML is pivotal for sectors like healthcare, finance, and manufacturing. This program is distinguished by its research-centric approach, fostering deep expertise and contributing to the global and Indian AI landscape.
Who Should Apply?
This program is ideal for candidates holding a Master''''s degree (M.Tech, MCA, M.E., MS) in Computer Science or a related engineering discipline, who possess a strong aptitude for research. It is suitable for aspiring academics, R&D professionals, and innovators seeking to contribute original knowledge and advancements in AI/ML technologies. Working professionals with relevant experience may also pursue this program on a part-time basis.
Why Choose This Course?
Graduates of this program can expect to secure leading R&D roles in technology companies, become faculty members at prestigious academic institutions in India and abroad, or launch their own AI-focused startups. The program cultivates skills essential for high-impact publications and patents. Entry-level salaries for AI researchers in India can range from INR 8-15 LPA, with experienced PhD holders commanding significantly higher packages in top-tier firms and research labs.
Student Success Practices
Foundation Stage
Master Research Methodology and Ethics- (Year 1)
Thoroughly engage with the Research Methodology coursework, focusing on quantitative and qualitative research design, statistical analysis, and academic ethics. Actively participate in discussions and seek guidance to apply these principles to potential AI/ML research problems.
Tools & Resources
SIU PhD Ordinance documents, Academic journals in research methods, Statistical software like R or Python libraries (SciPy, Statsmodels)
Career Connection
A strong foundation ensures rigorous and ethical research, leading to credible publications and a respected academic/research profile.
Deepen Foundational AI/ML Knowledge for Research- (Year 1)
For the specialized coursework, delve into advanced topics in AI/ML, focusing on understanding the mathematical underpinnings and limitations of various algorithms. Identify gaps in current literature and emerging trends relevant to your chosen sub-domain.
Tools & Resources
Leading AI/ML textbooks, Online courses (Coursera, edX, NPTEL for IIT courses), Research papers from top conferences (NeurIPS, ICML, CVPR, ACL)
Career Connection
Builds a robust technical base, enabling you to identify novel research problems and develop innovative solutions, crucial for R&D roles.
Initiate Research Problem Identification & Supervisor Collaboration- (Year 1)
Actively work with your assigned Doctoral Committee to refine your research problem, conduct initial literature reviews, and establish a clear research plan. Regularly communicate with your supervisor to align on expectations and methodology.
Tools & Resources
Research proposal templates, Reference management software (Mendeley, Zotero), Digital libraries (IEEE Xplore, ACM Digital Library, Scopus)
Career Connection
Early clarity and strong mentor relationships streamline the research process and are vital for successful thesis completion and future academic networking.
Intermediate Stage
Conduct Rigorous Experimentation and Analysis- (Year 2-3)
Systematically design and execute experiments, ensuring reproducibility and thorough analysis of results. Maintain meticulous records of methodologies, data, and findings. Document all code and experimental setups.
Tools & Resources
High-performance computing resources (if available), Version control systems (Git), Python/TensorFlow/PyTorch for ML experimentation, Jupyter Notebooks for documentation
Career Connection
Develops practical skills in scientific investigation and problem-solving, highly valued in industry R&D and academic research roles.
Engage in Academic Dissemination and Networking- (Year 2-3)
Attend and present research at national and international conferences, workshops, and seminars. Actively network with other researchers, faculty, and industry experts. Seek feedback on your work and explore potential collaborations.
Tools & Resources
Conference websites (e.g., AAAI, IJCAI, WACV), ResearchGate, LinkedIn for professional networking
Career Connection
Enhances visibility for your research, opens doors for post-doctoral positions, and establishes a professional network critical for career progression.
Develop a Strong Publication Strategy- (Year 2-3)
Target high-impact journals and conferences for publishing your research findings. Focus on writing clear, concise, and impactful papers. Be prepared for peer review and revise your work diligently based on feedback.
Tools & Resources
Journal metrics (Impact Factor, CiteScore), Grammar checkers (Grammarly), LaTeX for academic writing
Career Connection
A strong publication record is crucial for academic career advancement, securing research grants, and demonstrating research impact to potential employers.
Advanced Stage
Refine Thesis Writing and Defense Skills- (Year 4-6)
Dedicate significant time to writing your doctoral thesis, ensuring it presents original contributions, adheres to academic standards, and tells a compelling research story. Practice your thesis defense presentation extensively.
Tools & Resources
SIU Thesis Guidelines, Academic writing workshops, Mock defense sessions with peers and mentors
Career Connection
Successfully defending your thesis marks the culmination of your doctoral journey and signifies your readiness for independent research leadership.
Mentor Junior Researchers and Collaborate- (Year 4-6)
Take opportunities to mentor Master''''s or junior PhD students, sharing your knowledge and research experience. Engage in collaborative projects within your lab or department, demonstrating leadership and teamwork.
Tools & Resources
Internal departmental seminars, Research project management tools
Career Connection
Develops leadership, mentoring, and collaborative skills, highly valued in academic faculty positions, senior research roles, and team leadership in industry.
Strategize Post-PhD Career Path and Opportunities- (Year 4-6)
Actively explore and apply for post-doctoral fellowships, academic positions, or R&D roles in industry. Tailor your CV, cover letter, and research statement to specific opportunities. Leverage your network for referrals and insights.
Tools & Resources
University career services, Academic job boards (Chronicle of Higher Education, Inside Higher Ed), Industry job portals
Career Connection
Proactive career planning ensures a smooth transition from PhD studies to a fulfilling professional role, maximizing the impact of your doctoral qualification.
Program Structure and Curriculum
Eligibility:
- Master''''s Degree (e.g., M.Tech, MCA, M.E., MS) with at least 55% marks or equivalent grade, OR M.Phil Degree with at least 55% marks. Relaxation of 5% for SC/ST/OBC (non-creamy layer)/Differently-Abled candidates. Qualify in an entrance exam (e.g., SET/UGC-NET/CSIR-NET/GATE) or be exempt. Part-time option available for working professionals with minimum two years of relevant experience.
Duration: Minimum 3 years, Maximum 6 years (full-time)
Credits: Minimum 16 credits (for coursework) Credits
Assessment: Internal: Not explicitly defined for coursework, External: Evaluation by written examination at the end of the semester for coursework
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PHDRM01 | Research Methodology | Core | 4 | Fundamentals of Research, Research Design and Ethics, Data Collection and Analysis Techniques, Statistical Methods for Research, Report Writing and Publication Ethics, Intellectual Property Rights |
| PHDAIDL | Advanced Topics in Deep Learning | Specialization Core | 4 | Neural Network Architectures (Transformers, GANs), Optimization Techniques for Deep Learning, Deep Reinforcement Learning, Graph Neural Networks, Adversarial Machine Learning |
| PHDAIMLC | Advanced Machine Learning Concepts | Specialization Core | 4 | Causal Inference and Bayesian Networks, Probabilistic Graphical Models, Advanced Feature Engineering and Selection, Ensemble Learning and Boosting Algorithms, Unsupervised and Semi-Supervised Learning |
| PHDAISDA | AI in Specialized Domains and Applications | Specialization Core | 4 | Natural Language Processing (Advanced), Computer Vision (Advanced), Robotics and Autonomous Systems, AI for Healthcare and Bioinformatics, Ethical AI, Bias and Fairness in AI |
