Graduate Programmes
Digital Science in UBD offers three (3) graduate programmes
Master of Digital Science by research at SDS (School of Digital Science) are programmes that foster research in Digital Science and related disciplines. Master by Research in Digital Science includes the elaboration of short projects in a broad range of pure and applied research topics. The research projects must be original, incorporate modern techniques and methods, and contribute to the advancements of the fields of Digital Science. Candidates will work under the guidance and supervision of experienced academia specializing in Digital Science. Supervisors will provide mentorship, support, and expertise to ensure the successful execution of research projects.
The ultimate goal of the Master by Research in Digital Science program is to contribute to the advancement of the Digital Science field. Through their research endeavors, students are expected to generate new knowledge, insights, and solutions that have real-world applications and implications.
The program is designed primarily for appropriately qualified individuals interested in acquiring advanced knowledge and research skills in the domain of digital science with a focus on any topic of local, regional, or global relevance.
Master of Digital Science (MDSC) by Research Major Areas
- Master of Digital Science (Artificial Intelligence) by research
- Master of Digital Science (Computer Science) by research
- Master of Digital Science (Cybersecurity & Forensic) by research
- Master of Digital Science (Data Science) by research
- Master of Digital Science (Robotics) by research
Programme Details
Aims and Scope
The MSc Programme in Digital Science aims to make scientists with high level specialised training, in order to cover the increased needs of Industry in related aspects. Also, students wishing to continue their studies at a PhD level, will be able to prepare for the conduction of a PhD research on relevant topics. Also, students wishing to continue their studies at a PhD level will be able to prepare for the conduction of PhD research on relevant topics. The scope of the Programme is to provide students the necessary specific scientific information, as well as to train them to develop their skills and analytical capabilities.
Structure
Students conduct an approved research project, with the supervision of one or more staff members. Upon completion of their research, they submit a Thesis, which normally does not exceed 60,000 words.
Start Date
August and January
Duration of Programme
Full-Time: Min 12 months, Max 24 months
Part-Time: Min 24 months, Max 48 months
Period of Candidature:
Full-Time: 12 – 24 months
Part-Time: 24 – 48 months
Assessment
- Assessment includes examination of the thesis by internal and external examiners. As stipulated in the relevant UBD regulations the examiners may subject a candidate to an oral examination or any other test they think necessary to assess the acceptability of the thesis. Periodic assessment of the progress of the candidate is carried out as stipulated in the relevant UBD regulations.
Global public health has now greatly improved because of research and technological advancements in digital science, notably in the areas of big data and digital technology.
Predictive analytics on large-scale healthcare data, supervised and unsupervised machine learning, complex deep and transfer learning, biostatistics, behavioural science, epidemiology, non-communicable disease, infectious disease, health promotion, and evaluation are just a few examples of the major knowledge and skills that make up digital public health.
The programme is developed base on the demands from stakeholders including Ministry of Health. This programme is primarily intended for individuals with the necessary qualifications who are interested in expanding their knowledge and research abilities in any significant area of digital science and public health by concentrating on any issue of local, regional, or global significance.
Programme Details
Aims and Scope
To enable the student to obtain a thorough understanding of the principles, concepts, and research methods of a selected digital public health topic of local, regional, or global relevance.
Structure
Each candidate undertakes an approved research project and attends course if prescribed. Candidate has also to take at least eight (8) MCs from two (2) option modules for graduate studies offered by PAPRSB Institute of Health Sciences. Here are the modules:
HX-5301 Systematic Review and Meta-analysis in Health Sciences (4MCs)
HX-5302 Quantitative Research Methodology (4 MCs)
HX-5304 Data Analysis Methods in Health Research (4 MCs)
ZH-5101 Computation Foundation for Health Data (4 MCs)
ZH-5102 Machine learning for Healthcare Data (4 MCs)
ZH-5103 Predictive Analytics Healthcare Data (4 MCs)
Candidate is advised to complete these modules within the first year of candidature for full-time candidates, and the first two years for part-time candidates. The modules are assessed by 100% coursework. On completion of the research, the student submits a thesis that normally does not exceed 60,000 words. Prescribed courses can include placements, practical or research projects in a relevant facility.
Start Date
August and January
Duration of Programme
Full-Time: Min 12 months, Max 24 months
Part-Time: Min 24 months, Max 48 months
Period of Candidature:
Full-Time: 24 – 36 months
Part-Time: 48 – 72 months
Assessment
- Assessment comprises of examination of the thesis by internal and external examiners. As stipulated in the relevant UBD regulations, the examiners may subject a candidate to an oral examination or such other tests as they think necessary to assess the acceptability of the thesis.
Periodic assessment of progress of the candidate is carried out as stipulated in relevant UBD regulations.
Option modules are assessed by 100% coursework.
PhD programmes at the School of Digital Science (SDS) are programmes that foster advanced research in Digital Science and related disciplines.
The PhD programmes include the elaboration of original projects with an international research impact, which incorporate modern techniques and methods, in a broad range of pure and applied research topics. Candidates are expected to work diligently and they should be able to perform integrated research under the supervision of SDS staff members. They should be able to carry out analytical and experimental research and to synthesise and interpret the relevant data in a timely manner. The PhD candidates must also participate actively in research team meetings, as well as in Symposia and Conferences. The Programme is designed for qualified individuals, who wish to acquire advanced knowledge, as well as analytical and research skills, and significantly contribute to the advancement of Digital Science fields.
The programme is designed primarily for appropriately qualified individuals interested in acquiring advanced knowledge and research skills in the domain of digital science with a focus on any topic of local, regional, or global relevance.
Doctor of Philosophy (PhD) Major Areas
- PhD in Digital Science (Artificial Intelligence)
- PhD in Digital Science (Computer Science)
- PhD in Digital Science (Cybersecurity & Forensic)
- PhD in Digital Science (Data Science)
- PhD in Digital Science (Robotics)
Programme Details
Aims and Scope
The PhD Programme aims to enable students to obtain a thorough understanding of the principles, concepts, and research methods of digital science topics of local, regional, or global relevance. The scope of the Programme is to educate students to become independent researchers, as well as to train them to develop advanced scientific skills and analytical capabilities. The candidates are also expected to become capable to design scientific projects, to develop independent critical thinking and ability for proper scientific interpretations.
Structure
The student undertakes an approved research project and attends course modules if prescribed. On completing the research, the student submits a thesis that normally does not exceed 100,000 words.
Duration of Programme
Full-Time: Min 36 months, Max 60 months
Part-Time: Min 48 months, Max 84 months
Period of Candidature:
Full-Time: 36 – 60 months
Part-Time: 48 – 84 months
Assessment
Assessment includes examination of the thesis by internal and external examiners. As stipulated in the relevant UBD regulations the examiners may subject a candidate to an oral examination or any other test they think necessary to assess the acceptability of the thesis. Periodic assessment of the progress of the candidate is carried out as stipulated in the relevant UBD regulations.
Research interests in digital science include but not limited to the following areas:
- Intelligent Healthcare Systems
- Wireless Sensor Integration and Fusion
- Motion Capture ad Reconstruction
- Brain Machine Interface
- Virtual Interfacing Technologies
- Intelligent eLearning and web-based applications
- Biologically-inspired Robotics: Swarm robotics, Collective Decision Making, Animal vs. Animal
- Human-Robot Interaction: Human Safety in Human-Robot Cooperation, Human-Robot Swarm Interaction
- Large-Scale Autonomous Systems: Modeling and Control of Dynamical Systems including Manipulators, Mobile Robots, Underwater and Aerial Robots
- Modern Artificial Intelligence: Modular Robotics, Reconfigurable Building Blocks, Modular Sensor Networks, Tangible Games
- Problem Based Learning: ICT-based PBL and Multicultural PBL
- Compilers
- Cryptography and computer security
- Open source implementations
- Graphics and visualisation
- Database design and implementation
- Server security
- High performance computing
- Mobile programming
- Data mining: Cluster analysis, fuzzy clustering, biomedical or clinical informatics, applying heuristics
- Artificial intelligence, machine learning, deep learning
- Personal robots
- Ambient intelligence
You may find potential supervisors in the Staff section.
Scholarships are available for highly qualified candidates. Please refer to UBD Scholarship page for more information.
For more information, please contact the Programme Leader at office.sds@ubd.edu.bn.