The Doctor of Philosophy (PhD) in Artificial Intelligence at Madan Bhandari University of Science and Technology (MBUST) is a three-year, full-time doctoral program focused on advanced computing, machine learning, intelligent systems, and independent scientific research.
The program combines technical coursework, research methods, group projects, specialized electives, and doctoral thesis research. Its academic coverage includes machine learning, computer vision, natural language processing, deep learning, intelligent systems, and applications of artificial intelligence in different sectors.
Research areas include language and vision models, precision agriculture, cybersecurity, biomedical applications, disaster response, and crisis management. The program also addresses ethical considerations and the application of artificial intelligence to problems associated with the Sustainable Development Goals (SDGs).
MBUST offers the PhD through its Digital Technology academic area at Chitlang, Thaha Municipality, Makawanpur District, Bagmati Province, Nepal.
| Particulars | Details |
|---|---|
| Program | Doctor of Philosophy in Artificial Intelligence |
| Abbreviation | PhD in AI |
| University | Madan Bhandari University of Science and Technology (MBUST) |
| Academic Level | Doctoral |
| Duration | 3 years |
| Study Mode | Full-time, research-based |
| Academic Structure | 6 semesters |
| Total Credits | 76 |
| Core Coursework | 18 credits |
| Technical Electives | 6 credits |
| Group Project | 2 credits |
| Thesis Credits | 50 |
| Major Subjects | Machine Learning, Computer Vision, Natural Language Processing, Deep Learning |
| Research Component | Independent doctoral thesis |
| Location | Chitlang, Makawanpur, Bagmati Province, Nepal |
The PhD in Artificial Intelligence develops advanced research competencies in computational methods, intelligent systems, and their applications.
Students study the theoretical foundations of artificial intelligence alongside specialized computational techniques and research methodologies. The program emphasizes independent investigation, experimental evaluation, scientific communication, and the development of research findings.
Its academic structure combines technical subjects with research addressing problems in agriculture, healthcare, environmental management, business, and governance.
The Digital Technology research area at MBUST also includes Data Science, Internet of Things, and Cyber Security laboratories. These subjects are connected with the university's wider computing research activities.
The PhD places particular emphasis on developing the ability to investigate artificial intelligence problems through systematic scientific research rather than relying exclusively on established computational methods.
The PhD in Artificial Intelligence aims to develop independent researchers with advanced knowledge of computing and artificial intelligence.
Its principal objectives include:
Developing advanced understanding of machine learning, deep learning, computer vision, and natural language processing.
Investigating computational methods for intelligent systems.
Designing and evaluating artificial intelligence models and applications.
Conducting independent scientific research.
Examining ethical and societal considerations associated with artificial intelligence.
Applying computational techniques to research problems in agriculture, healthcare, environmental management, and related fields.
Developing scientific communication and doctoral research competencies.
Producing an independent doctoral thesis.
The program connects technical specialization with research questions involving practical applications and the assessment of computational methods.
The PhD in Artificial Intelligence follows a six-semester academic structure carrying 76 credits.
The curriculum combines core technical courses, specialized electives, a group project, and doctoral thesis research.
| Academic Component | Credits |
|---|---|
| Core Coursework | 18 |
| Technical Electives | 6 |
| Group Project | 2 |
| Doctoral Thesis | 50 |
| Total | 76 |
The doctoral thesis accounts for 50 credits, making independent research the largest academic component of the program.
The first semester introduces the principal computational and research subjects.
| Course Code | Course Title | Credits |
|---|---|---|
| AI-CR-501 | Machine Learning | 3 |
| AI-CR-502 | Practical Data Science with Python | 2 |
| AI-CR-503 | Group Project in People-Centred AI | 2 |
| AI-CR-504 | Research Methods for Intelligent Systems | 1 |
| GC-NC-550 | Entrepreneurship, Scientific Communication and Leadership | 0 |
Students also begin group project activities, including project formation and assignment.
The second semester introduces computer vision, natural language processing, development policy, and a technical elective.
| Course Code | Course Title | Credits |
|---|---|---|
| AI-CR-550 | Computer Vision | 3 |
| AI-CR-551 | Natural Language Processing | 3 |
| AI-EL-561–570 | Technical Elective I | 2 |
| GC-CR-501 | Development Policy | 3 |
| AI-NC-553 | Case Studies in Ethics and Fairness in AI | 0 |
Group project activities continue alongside specialized coursework and preparation for doctoral research.
The third semester includes Advanced Topics in Deep Learning (AI-CR-601), a technical elective, and doctoral research.
Students prepare their research proposal for submission to the advisory committee and begin the planned thesis investigation.
The fourth semester concentrates on doctoral thesis work and an additional technical elective.
Students continue their research and present their thesis progress.
The fifth semester focuses on doctoral thesis research.
Research activities continue within the selected specialization, accompanied by a thesis progress presentation.
The final semester is devoted to doctoral research completion.
The academic progression includes a comprehensive examination and thesis defense.
Machine Learning examines computational methods for building models from data.
Its academic coverage includes probabilistic methods, optimization, generalization, neural networks, kernel methods, ensemble methods, and deep learning.
This course provides practical preparation in computational data processing and analysis.
Students work with different data formats, computational methods, algorithms, and visualization techniques.
Computer Vision covers methods for processing and interpreting image and video information.
The subject includes image filtering, feature detection, object detection, image segmentation, and classification methods.
Natural Language Processing examines computational methods for analyzing and processing human language.
Topics include syntax, semantics, entity recognition, information extraction, word representations, dialogue systems, and language models.
This course extends foundational machine learning into specialized neural network methods.
Its academic content includes graph-based neural networks, diffusion models, and other advanced learning architectures.
The doctoral curriculum includes six technical-elective credits.
Elective study supports specialization in areas related to the student's doctoral research.
The Artificial Intelligence academic portfolio includes subjects such as reinforcement learning, multi-agent systems, graphical models, artificial intelligence in agriculture, connected devices, healthcare applications, and industrial automation.
Technical electives provide opportunities to connect computational methods with particular research problems.
Research includes large language models (LLMs) and vision-language models (VLMs).
These subjects examine computational systems that process language, images, or combinations of different information types.
Research interests include model development and adaptation for specific applications.
Artificial intelligence research in agriculture addresses crop monitoring, yield prediction, and agricultural resource management.
This area connects computational modeling with agricultural data and production-related research questions.
Research covers artificial intelligence applications in cybersecurity, disaster response, early warning systems, and crisis-related information analysis.
The field also includes computational approaches relevant to infrastructure planning and disaster management.
Biomedical research examines artificial intelligence methods related to healthcare systems and biomedical information.
The program identifies healthcare and biomedical applications among its research areas.
Human–wildlife conflict is included within the university's identified artificial intelligence research interests.
The subject connects computing research with problems involving interactions between human activities and wildlife.
Independent research is the principal component of the PhD in Artificial Intelligence.
The thesis involves investigation of a selected research problem through appropriate scientific and computational methods.
Students undertake research proposal development, experimental or analytical investigation, evaluation of findings, and preparation of doctoral research outputs.
The academic structure also incorporates research progress presentations, a comprehensive examination, and thesis defense.
The program includes ethical considerations within its academic and research structure.
Students examine ethics and fairness in artificial intelligence through a compulsory non-credit course.
Research applications are also connected with the Sustainable Development Goals, particularly through problems involving agriculture, health, environmental management, and community needs.
These components encourage assessment of artificial intelligence applications beyond computational performance alone.
The PhD provides advanced research preparation for activities involving artificial intelligence, machine learning, natural language processing, computer vision, and computational systems.
Its academic coverage is relevant to university research, scientific institutions, technology development, and specialized computational work.
The research areas also connect with agriculture, healthcare, environmental management, cybersecurity, and industrial applications.
Graduates may pursue academic research, university teaching, specialist technical research, or technology-related entrepreneurial activities, depending on their research experience, professional competencies, and institutional requirements.
It is a three-year, research-based doctoral program covering machine learning, computer vision, natural language processing, deep learning, specialized research, and independent doctoral thesis work.
The program follows a three-year, full-time academic structure consisting of six semesters.
The program carries 76 credits, including 18 core-course credits, six technical-elective credits, two group-project credits, and 50 thesis credits.
Research includes language and vision models, precision agriculture, cybersecurity, crisis management, biomedical applications, and human–wildlife conflict.
Yes. The doctoral curriculum includes six technical-elective credits supporting the student's research specialization.
Yes. Independent doctoral research is the principal component of the program, with 50 credits allocated to thesis work.
The academic progression includes research proposal submission, thesis progress presentations, a comprehensive examination, and thesis defense.
The program is offered at Madan Bhandari University of Science and Technology in Chitlang, Makawanpur District, Bagmati Province, Nepal.
