Doctor of Philosophy in Natural Language Processing - Artificial Intelligence
Mohamed bin Zayed University of Artificial Intelligence - MBZUAI
Key Information
Campus location
Abu Dhabi, United Arab Emirates
Languages
English
Study format
On-Campus
Duration
4 years
Pace
Full time
Tuition fees
Request info
Application deadline
Request info *
Earliest start date
Aug 2024
* deadline for international students/deadline for UAE nationals: May 30th, 2024 | Application start date: Sep 1st, 2023
* no tuition fees + scholarship
Introduction
Natural language processing (NLP) focuses on system development that allows computers to communicate with people using everyday language. Natural language generation systems convert information from the computer database into readable or audible human language and vice versa. Such systems also enable sophisticated tasks such as inter-language translation, semantic understanding, text summarization and holding a dialog. The key applications of NLP algorithms include interactive voice response applications, automated translators, digital personal assistants (e.g., Siri, Cortana, Alexa), chatbots, and smart word processors.
Alumni Statistics
Admissions
Curriculum
The minimum degree requirements for the Doctor of Philosophy in Natural Language Processing is 60 credits, distributed as follows:
| Core courses | Number of courses | Credit hours |
| Core | 3 | 12 |
| Electives | 3 | 12 |
| Research thesis | 1 | 36 |
| Internship | At least one internship of up to four-months duration must be satisfactorily completed as a graduation requirement | 0 |
Core courses
The Doctor of Philosophy in Natural Language Processing is primarily a research-based degree. The purpose of coursework is to equip students with the right skillset, so they can successfully accomplish their research project (thesis). Students are required to take AI701, MTH701 and NLP701 as mandatory courses. They can select three electives.
| Code | Course Title | Credit Hours |
| AI701 | Foundations of Artificial Intelligence | 4 |
| MTH701 | Mathematical Foundations of Artificial Intelligence | 4 |
| NLP701 | Natural Language Processing | 4 |
| NLP702 | Advanced Natural Language Processing | 4 |
| NLP703 | Speech Processing | 4 |
| NLP704 | Deep Learning for Language Processing | 4 |
| NLP705 | Topics in Advanced Natural Language Processing | 4 |
| NLP706 | Advanced Speech Processing | 4 |
Elective courses
Students will select a minimum of three elective courses, with a total of 12 (or more) credit hours. Two must be selected from List A and one must be selected from List A or B based on interest, proposed research thesis, and career aspirations, in consultation with their supervisory panel. The elective courses available for the Doctor of Philosophy in Natural Language Processing are listed in the tables below:
List A
| Code | Course Title | Credit Hours |
| NLP702 | Advanced Natural Language Processing | 4 |
| NLP703 | Speech Processing | 4 |
| NLP704 | Deep Learning for Language Processing | 4 |
| NLP705 | Topics in Advanced Natural Language Processing | 4 |
| NLP706 | Advanced Speech Processing | 4 |
List B
| Code | Course Title | Credit Hours |
| AI702 | Deep Learning | 4 |
| CV701 | Human and Computer Vision | 4 |
| CV702 | Geometry for Computer Vision | 4 |
| CV703 | Visual Object Recognition and Detection | 4 |
| CV704 | Advanced Techniques in Low-Level Vision | 4 |
| CV705 | Advanced 3D Computer Vision | 4 |
| CV706 | Advanced Techniques in Visual Object Recognition and Detection | 4 |
| CV707 | Digital Twins | 4 |
| DS701 | Data Mining | 4 |
| DS702 | Big Data Processing | 4 |
| HC701 | Medical Imaging: Physics and Analysis | 4 |
| ML701 | Machine Learning | 4 |
| ML702 | Advanced Machine Learning | 4 |
| ML703 | Probabilistic and Statistical Inference | 4 |
| ML704 | Machine Learning Paradigms | 4 |
| ML705 | Topics in Advanced Machine Learning | 4 |
| ML706 | Advanced Probabilistic and Statistical Inference | 4 |
| ML707 | Smart City Services and Applications | 4 |
| ML708 | Trustworthy Artificial Intelligence | 4 |
| MTH702 | Optimization | 4 |
Research thesis
The Ph.D. thesis exposes students to cutting-edge and unsolved research problems in the field of natural language processing, where they are required to propose new solutions and significantly contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of three to four years.
| Code | Course Title | Credit Hours |
| NLP799 | Natural Language Processing Ph.D. Research Thesis | 36 |
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Ideal Students
STEM major students with GPA above 3.2/4.0
Rankings
CS Rankings in a Glance
- 18th in the field of AI in CS Rankings globally
- 28th in the field of ML in CS Rankings globally
- 16th in the field of CV in CS Rankings globally
- 19th in the field of NLP in CS Rankings globally
Program Outcome
Upon completion of the program requirements, the graduate will be able to:
- Develop a deep and comprehensive understanding of cutting-edge NLP algorithms with applications to real-life scenarios
- Implement, evaluate, and benchmark existing state-of-the-art in NLP scholarly publications and weigh-in on their respective pros and cons
- Grow capabilities to identify open research problems, the gaps in the existing body of knowledge, and formulate new research questions
- Independently develop innovative solutions, through extensive research and scholarship, to resolve unsolved research problems in high-impact real-life applications of NLP
- Demonstrate expert knowledge and highly specialized cognitive and creative skills in NLP to deliver state-of-the-art solutions to existing open research problems
- Pursue an NLP project either independently, or as part of a team in a collegial manner, with minimal supervision
- Initiate, manage, and complete research manuscripts that demonstrate expert self-evaluation and advanced skills in scientifically communicating highly complex ideas
- Develop highly sophisticated skills in initiating, managing, and completing multiple project reports and critiques, on a variety of NLP problems, that demonstrate expert understanding and advanced skills in communicating highly complex ideas
Program Tuition Fee
Career Opportunities
AI is permeating every industry. At recent employer engagement events at MBZUAI, there has been representation from multiples sectors including (but not limited to):
- Aviation, consultancy, education, energy, finance, government entities, healthcare, media, oil and gas, security and defense, research institutes, retail, telecommunications, transportation and logistics, and startups.
Recent job opportunities advertised via the MBZUAI Student Careers Portal include (but not limited to):
- AI solution architect, AI solution engineer, algorithmic engineer, data analyst, data engineer, data scientist, data strategy consultant, full stack software engineer, full stack web developer, predictive analytics researcher, and senior data scientist – consultant.
Other career opportunities could include (but not limited to):
- Applied scientist, analytics engineer, augmented/virtual reality, autonomous cars, biometrics and forensics, chief data officer, data platform leadership, data journalist, data and AI technical sales specialist, growth analytics / engineers, manager: AI and cloud services planning, machine learning engineers, product manager: AI and data analytics, product data scientist, product analyst, remote sensing, research assistants, security and surveillance, senior software engineer, and VP data.