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PHD in Computer Science And Technology at Indian Institute of Engineering Science and Technology, Shibpur
Howrah, West Bengal
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About the Specialization
What is Computer Science and Technology at Indian Institute of Engineering Science and Technology, Shibpur Howrah?
This Computer Science and Technology PhD program at IIEST Shibpur focuses on advanced research and innovation in core and emerging areas of computing. It addresses critical challenges relevant to the Indian technology landscape, fostering deep theoretical understanding and practical application skills. The program''''s interdisciplinary nature and strong emphasis on novel contributions are key differentiators, preparing scholars for high-demand roles in research and development across various Indian industries.
Who Should Apply?
This program is ideal for postgraduate students with a strong academic background in Computer Science or related fields who aspire to pursue a career in cutting-edge research. It also suits working professionals from IT/software sectors in India looking to transition into R&D roles, academia, or establish technology startups. Candidates are expected to have a solid foundation in computer science fundamentals and a keen interest in solving complex computational problems.
Why Choose This Course?
Graduates of this program can expect to pursue fulfilling careers as research scientists in government labs and corporate R&D divisions, lead innovation in Indian tech companies, or contribute to academia as professors. Salary ranges for PhD holders in India typically start from INR 8-15 LPA for entry-level research roles, escalating significantly with experience. The program aligns with the growing demand for highly skilled researchers in AI, Data Science, Cybersecurity, and Cloud Computing in the Indian market.
Student Success Practices
Foundation Stage
Master Research Methodology and Core Concepts- (Initial 1-2 Semesters)
Actively engage with the PhD coursework, especially Research Methodology, to build a strong foundation in academic writing, experimental design, and data analysis. Simultaneously, revisit and solidify core Computer Science concepts essential for your chosen research domain.
Tools & Resources
Institute Library databases (IEEE Xplore, ACM Digital Library), NPTEL courses on advanced topics, ResearchGate for paper discussions
Career Connection
A robust methodological foundation is critical for publishing high-quality papers and successfully defending your research, which are key to academic and R&D careers.
Identify and Refine Research Problem- (Initial 1-2 Semesters)
Work closely with your supervisor to identify a novel, impactful, and feasible research problem. Conduct an exhaustive literature review, participate in departmental seminars, and brainstorm ideas to narrow down your focus. Clearly define your research questions and objectives early on.
Tools & Resources
Google Scholar, Connected Papers, Mendeley/Zotero for reference management, Brainstorming sessions with peers/faculty
Career Connection
A well-defined research problem is the backbone of a successful PhD, attracting funding and enabling groundbreaking contributions, crucial for both academic recognition and industrial innovation.
Develop Strong Programming and Simulation Skills- (Ongoing from Semester 1)
Enhance your programming proficiency in languages like Python, Java, or C++ and gain expertise in relevant simulation or analysis tools. This is crucial for implementing experimental setups, processing data, and developing prototypes for your research.
Tools & Resources
LeetCode/HackerRank for coding practice, GitHub for version control, TensorFlow/PyTorch for ML research, NS-3/OMNeT++ for network simulations
Career Connection
Practical skills are highly valued in R&D roles. Demonstrating strong implementation capabilities can lead to better research outcomes and attractive opportunities in tech companies.
Intermediate Stage
Regularly Publish and Present Research- (After Coursework, throughout Research Phase)
Aim to publish your preliminary findings in reputable national/international conferences and journals. Regularly present your work in departmental seminars or workshops to get feedback and refine your research direction. This builds your research profile and presentation skills.
Tools & Resources
LaTeX for paper writing, Microsoft PowerPoint/Google Slides for presentations, Journal ranking databases (Scopus, Web of Science)
Career Connection
Publications are the primary currency in academia and a strong asset for industrial research roles. Presenting builds confidence and networking opportunities, essential for career progression.
Seek Interdisciplinary Collaborations and Workshops- (Mid-way through Research Phase)
Explore collaboration opportunities with researchers from other departments or institutions. Attend national and international workshops, summer/winter schools, and advanced training programs to learn new techniques and expand your network. This broadens your research perspective.
Tools & Resources
Research groups within IIEST and other IITs/NITs, International conference websites for workshops, LinkedIn for professional networking
Career Connection
Collaborations lead to richer research, joint publications, and a wider professional network, opening doors to post-doctoral positions, joint ventures, and diverse career paths.
Prepare for Comprehensive Examination- (As per Institutional Timeline (usually after coursework))
Dedicate focused time to prepare for your comprehensive viva-voce, which assesses your breadth of knowledge in Computer Science and your specific research area. Review core concepts, past question papers (if available), and present your research progress effectively.
Tools & Resources
Textbooks on fundamental CS subjects, Review notes from coursework, Mock viva sessions with peers/seniors
Career Connection
Passing the comprehensive exam demonstrates mastery of the field, a crucial milestone for PhD progression and a testament to your foundational knowledge for future employers.
Advanced Stage
Efficient Thesis Writing and Defense Preparation- (Final 1-2 Years of Program)
Systematically document your research findings, methodology, and contributions in a clear, concise, and academically rigorous thesis. Start writing early, revise regularly with supervisor feedback, and meticulously prepare for your final viva-voce defense, anticipating potential questions.
Tools & Resources
LaTeX for thesis formatting, Grammarly/Turnitin for language and plagiarism checks, Practice defense presentations
Career Connection
A well-written thesis and a strong defense are essential for earning the degree and establishing your credibility as an independent researcher, influencing job prospects in academia or industry.
Proactive Career Planning and Networking- (Final Year of Program)
Actively explore post-PhD career options, whether in academia, industry R&D, or entrepreneurship. Network with professionals in your target fields, attend career fairs, and tailor your CV/resume to highlight your research skills and contributions.
Tools & Resources
IIEST Placement Cell, LinkedIn for job searches and networking, Conferences for employer interaction
Career Connection
Early career planning and a strong network are vital for a smooth transition from PhD to employment, securing desired roles and leveraging your advanced expertise.
Mentor Junior Researchers and Collaborate Post-PhD- (Throughout Advanced Stage and Post-PhD)
Engage in mentoring junior PhD or M.Tech students, sharing your research experience and guiding them. Look for opportunities to collaborate on new projects or extend your thesis work post-PhD, fostering a continuous research trajectory.
Tools & Resources
Departmental mentorship programs, Research grant opportunities, Alumni networks
Career Connection
Mentoring develops leadership skills, while continued collaboration enhances your impact and visibility in the research community, crucial for long-term career growth and establishing a research legacy.
Program Structure and Curriculum
Eligibility:
- Master''''s degree in Engineering/Technology or equivalent with minimum 6.5 CGPA/60% marks OR Master''''s degree in Science/Humanities with minimum 6.5 CGPA/60% marks AND valid GATE/UGC-NET/CSIR-NET scores. B.Tech/BE with minimum 8.0 CGPA/75% marks and valid GATE score or Institute Fellowship are also eligible. Other specific conditions apply as per PhD Ordinance 2023.
Duration: Ordinarily 2 years (for M.Tech/ME/M.Phil holders) to 3 years (for B.Tech/BE/M.Sc/MA/MCA holders), maximum 6 years
Credits: Minimum 8 credits (for M.Tech/ME/M.Phil holders), Minimum 12 credits (for B.Tech/BE/M.Sc/MA/MCA holders) Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester coursework
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS 4005 | Research Methodology and Scientific Communication | Core (Highly Recommended for PhD Coursework) | 4 | Introduction to Research, Research Design and Methods, Data Collection and Analysis, Scientific Writing and Presentation, Ethics in Research |
| CS 4001 | Advanced Data Structures & Algorithms | Core (Elective for PhD Coursework) | 4 | Advanced Data Structures, Algorithm Design Paradigms, Graph Algorithms, Computational Complexity, Approximation Algorithms |
| CS 4002 | Advanced Computer Architecture | Core (Elective for PhD Coursework) | 4 | Processor Design, Memory Hierarchy, Pipelining and Parallelism, Multicore Architectures, GPU Architectures |
| CS 4003 | Advanced Operating Systems | Core (Elective for PhD Coursework) | 4 | Distributed Operating Systems, Real-time Operating Systems, Virtualization, Security in OS, OS for Cloud Computing |
| CS 4004 | Advanced Database Systems | Core (Elective for PhD Coursework) | 4 | Distributed Databases, NoSQL Databases, Database Security, Query Optimization, Big Data Databases |
| CS 4006 | Advanced Computer Networks | Core (Elective for PhD Coursework) | 4 | Network Architectures, Software Defined Networking, Network Security Protocols, Wireless and Mobile Networks, Internet of Things Networking |
| CS 4021 | Machine Learning | Elective (Elective for PhD Coursework) | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Neural Networks, Model Evaluation |
| CS 4038 | Deep Learning | Elective (Elective for PhD Coursework) | 4 | Neural Network Architectures, Convolutional Neural Networks, Recurrent Neural Networks, Generative Models, Deep Learning Applications |
| CS 4033 | Big Data Analytics | Elective (Elective for PhD Coursework) | 4 | Big Data Technologies, Hadoop Ecosystem, Spark Programming, Stream Processing, Big Data Storage and Management |
| CS 4037 | Blockchain Technology | Elective (Elective for PhD Coursework) | 4 | Cryptographic Primitives, Distributed Ledger Technologies, Consensus Mechanisms, Smart Contracts, Blockchain Applications |
