PhD Computer Science Syllabus, Subjects, Entrance Exam, Yearly, Semester, Projects, Books

PhD in Computer Science Subjects 

• Research Methodology - This includes choosing methods that are appropriate for research aims and objectives and understanding the limitations of particular research methods. Topics like Meaning and objective of Research Methodology, Motivation in research, types of research, different Research Approaches, the significance of the research, Research Methods versus Methodology.
• Performance Analysis of Wireless Sensor Network - Students will get to know more about Localization, Fundamentals of Genetic Algorithms, Genetic Modelling etc 
• Short Range Wireless Communication-Students will get to know about to know the calculation of different performance metrics corresponding different communication systems.
• Methods of Advanced Wireless Communication Systems- This will guide students regarding Understanding various short-term and long-term communication systems. Topics like the Evolution of mobile radio communications, paging systems, Cordless telephone systems, and comparison of various wireless systems. 

See Also:

Ph.D. in Computer Science Syllabus

A detailed description of the topics in Computer Science are listed in tabular form below:

PhD in Computer Science Books

Phd Entrance Exam Syllabus for Computer Science 

JNU Ph.D. Computer Science Entrance Exam Syllabus

JNUEE is conducted by National Testing Agency (NTA) to offer admissions to aspirants in undergraduate and postgraduate programs. The Syllabus of JNUEE varies according to the course. The detailed syllabus will be provided by the conducting authority on the date of notification. 

IGNOU PhD Computer Science Syllabus

The Indira Gandhi National open university offers many courses for candidates who clear NTA IGNOU PhD . The Entrance exam holds for 70% weightage and 30% weightage for personnel interviews It is important that applicants must start preparing for the exam right after submitting their application to use their time effectively.

Ph.D. Computer Science Course Structure 

Ph.D. Computer Science course takes three to five years to complete. The curriculum is divided into six to ten semesters and the exam takes place annually. There are many cores and elective courses offered in the Ph.D. Computer Science program. This course teaches students about research terminology, ethical research principles, ethical challenges and approval processes. Students will be taught about quantitative, qualitative and mixed methods approaches to research.

Ph.D. in computer science includes a research project that must be completed by the end of the semester in order to gain practical and theoretical skills. The course structure of the computer science program is as follows:

• Theoretical knowledge
• VI to X Semesters
• Core Subjects
• Elective Subjects
• Practical Workshops
• Research Project/Thesis Submission
• Pedagogical methods 
• case discussion and assignment

Ph.D. Computer Science Teaching Methodology and Techniques

In this course, while teaching computer science new technologies are integrated that are beneficial for students. The skills of students are enhanced for the workplace by different teaching methods. The teaching methods that are used are listed below:

• Traditional Classroom Method
• Case discussion and assignment
• Class Projects
• Problem-Solving Approach
• Industrial Visit
• Computer Assisted Learning
• Audio Visuals
• Virtual Lab Exercises
• Brainstorming
• Discussion Method
• Seminars

Ph.D. Computer Science Projects 

Ph.D. in computer science projects is designed to help students to gain theoretical and practical skills. It will teach students about factors that influence the success of research projects and the strategies on how to implement them. The list of most popular Phd Computer Science project topics is stated below:

1. Causal Models for Mental Health Interventions that are Interactive.
2. Development of Graph- Embedding Techniques for Biomedical Knowledge Graphs.
3. Automated Experimental Design with Humans in the Loop.
4. Impact of Visual Images on Verbal Fluency in People Living with Dementia.
5. Performance evaluation of AI and IoT-enabled Smart Wheelchair
6. (MRD DTP) Understanding how psychedelic drugs affect the brain and behavior in rodents
7. The Learning of User Models in Human-in -the Loop Machine Learning.
8. Secure and Privacy-Preserving FAIR Data Vault Aggregation and Analysis.
9. Masters by Research Sponsored by Business with SDG Changemakers.
10. Studying Fall Risk in Parkinson’s Disease Patients in Real-Life Settings.
11. Robert Gordon University Presents Natural Language Generation for the ISee Explainable AI Platform.
12. The Development of an Intervention to Protect Older People from Cybercrime.