Ph.D. in AiCE Degree Requirements

The Ph.D. in Artificial Intelligence & Computer Engineering (AiCE) program at CMKL equips students with the expertise to pursue new frontiers in AI, Computer Engineering, and Innovation. The Ph.D. in AiCE program lasts five years (10 full-time semesters). The maximum period allowed to complete the Ph.D. degree requirements is six years, beginning with the successful completion of the Ph.D. Qualifying Examination. The PH.D. in AI and Computer Engineering required students to satisfy multiple requirements before the doctoral degree was certified.

Through artificial intelligence, computer engineering and an entrepreneurial perspective, AiCE graduates can assist our society in tackling challenges that will propel the future development of Thailand and Southeast Asia.

The requirements addressed in this section include:

Below is an example of a student’s plan of study over the duration of the AiCE Ph.D. program. Please note that this plan is an example and may vary based on whether the student has a master’s degree prior to enrolling, on conversations between the student and his or her advisor, and on which AiCE Ph.D. program the student is enrolled in..

Students pursuing a Ph.D. in AI & Computer Engineering will be able to:


Students with a bachelor's degree and an interest in computer engineering or a related discipline who want to work with AI are encouraged to apply. 


​​Students in the Ph.D. program without a M.S. degree and students in the Ph.D. program who have an AiCE M.S. degree from CMKL must take a total of eight AiCE or related courses (totaling 96 units) at CMKL. At least seven of these eight courses must be approved graduate-level courses. 

Students in the Ph.D. program who have earned a M.S. degree elsewhere (outside of CMKL’s AiCE department) must take a total of four AiCE or related courses (totaling 48 units) at CMKL. At least three of these four courses must be approved graduate-level courses.Students who received an M.S. in AiCE from CMKL may use their M.S. courses to count toward Ph.D. requirements.

Coding Bootcamp (CMKL 41-600) – 12 Units 
This course provides an intensive coding program that equips students with essential coding skills.

High Performance Computing for AI Application
(CMKL 41-605) – 12 Units   
This course explores the infrastructure necessary to support AI applications, including both on-premise and cloud-based high-performance computing (HPC) setups. Students will learn the programming paradigms used to facilitate AI applications.

Natural Language Processing (CMKL 41-611) – 12 Units     
In this course, students will delve into natural language processing, focusing on techniques and algorithms used to understand and process human language using AI methods.

Foundation of Computer (CMKL 41-613) – 12 Units 
This course emphasizes a programmer’s view of how computer systems run programs, collect information, and communicate. This encourages and helps students to become more effective and efficient programmers, particularly in handling issues of performance, portability, and robustness by teaching them the basic concepts underlying all computer systems (e.g., compilers, networks, operating systems, and computer architecture). 

Introduction to Information Security (CMKL41-631) – 12 Units 
The course provides the foundation of information security in detail of some important technical and policy. The significant goal of the course is to encourage students to understand a security engineering perspective on information systems and consider technical, economic, and policy factors. 

Foundations of Software Engineering (CMKL 41-652) – 12 Units 
In this course, students will get to understand computer software engineering paradigms that shaped the software industry over the past few decades. The course will emphasize the fundamental disciplines of computer software engineering together with engineering hands-on practices that crosscut systems, projects, and perspectives of the user. 

Software Requirements and Interaction Design (CMKL41-658) – 12 Units 
This course refers to computer software design challenges by integrating two disciplines: requirements engineering and interaction design. Students will get an understanding of how to combine user research, design-based ideation and validation, and requirements definition, within an agile software development process. 

Introduction to Machine Learning for Engineers (CMKL41-661) – 12 Units 
Machine learning has become a buzzword for over a decade now and has integrated itself deeply as one of the core pillars of digital transformation. This course makes you understand the definition of machine learning and emphasis AI computer engineering applications. 

Hardware/Software Co-design (CMKL 41-701) – 12 Units 
This course explores how software and hardware come together to implement computer systems. The course will be extremely hands-on, with weekly development cycles. Students will learn a new concept within the language/processor stack (e.g., parsing) and will be expected to implement it by the following week. 

Introduction to Computer Security (CMKL41-730) – 12 Units 
This course emphasizes a principled introduction to defending against hostile adversary techniques in modern computer systems and computer networks. The topics covered operating system security; network security, including cryptography and cryptographic protocols, firewalls, and network denial-of-service attacks and defenses; user authentication technologies; security for network servers; web security; and security for mobile code technologies (e.g., Java and JavaScript).  

Computer Architecture and Systems (CMKL41-742) – 12 Units
This course begins with a review of traditional, sequential computer architecture concepts.  

Packet Switching and Computer Networks (CMKL41-756) – 12 Units 
This course is intended to provide an understanding of the fundamental concepts in current and future computer networks.  

Network Management and Control (CMKL41-757) – 12 Units 
This course teaches the fundamentals of broadband networks. Broadband networks differ from existing communication networks in many ways, and these issues will be addressed in the course. 

Deep Learning (CMKL41-785) – 12 Units  
This course delves into deep learning techniques, covering advanced algorithms and methodologies used in training deep neural networks. Students will gain expertise in the field of deep learning.

Image and Video Processing (CMKL41-793) – 12 Units
 This course focuses on signal processing techniques for 2D (images) and 3D (videos) signals. It extends 1D signal processing techniques and specializes in image and video processing.  

Research, Entrepreneurship and Innovation (CMKL41-900) – 12 Units 
This unique course for AiCE program introduces students to explore the connections between research, entrepreneurship and innovation. Students will be introduced to industries and tech communities. 

Research and Development (CMKL41-910) – 36 Units 
Students in AiCE programs will have the opportunity to participate in real-world supervised research and development projects.   

Internship for Graduate (CMKL41-995) – Variable
Experiential learning experiences are key educational possibilities for graduate students in the Artificial Intelligence and Computer Engineering department. An internship, which is usually conducted during the summer, is one such alternative. 

Graduate Teaching Internship (CMKL41-999) – 12 Units 
The Teaching Internship for AI and Computer Engineering MS Students represents the capstone or culminating experience at CMKL University in the preparation of prospective lecturers as knowledgeable, reflective practitioners and emerging leaders who conduct themselves ethically and professionally.  

Breadth Area Requirement

The AiCE Program has defined four technical areas (plus an ‘Other’ area) to insure breadth of knowledge for the Ph.D. degree. Students are required to select one of these areas to fulfil their breadth requirements. The breadth areas are:

Each Ph.D. candidate must take at least one graduate class from at least four of these areas to fulfil the breadth requirement. Students must receive a "B-" grade or better in these courses.Students may be able to count one course taken during a previous degree toward the breadth course requirement by petitioning AiCE's Graduate Studies Committee. If the petition is accepted, only two more breadth areas must be satisfied. However, students will still be held to the same course requirements.

Qualifying Exam Requirement

Students who are working towards a Ph.D. degree are required to take the Ph.D. Qualifying Examination. The Ph.D. Qualifying Examination tests the student’s ability to think, speak, and write. Students must read and analyze three technical papers that define the examination topic area. Students then write a review paper and orally present this review to a faculty examining committee. 

Teaching Internship

All AiCE Ph.D. students are required to complete two Teaching Internships (“TI”) at CMKL – over the course of the Ph.D. program. These Teaching Internships are unpaid and students will receive a letter grade reflecting their performance. Students must receive a “B” or better in the TI course to receive credit for completing the teaching internship. The TI program is coordinated through the Academic Services Office. All students must complete a teaching assistant application for the TI to be formally recognized by the department. The Academic Services Office will work with the student to enroll him or her in the correct course representing their work as a TI prior to the start of the semester. 


All Ph.D. students are required to prepare a dissertation proposal within four semesters following the successful completion of the Ph.D. qualifying examination. This time clock begins with the semester following the qualifying examination. If a student leaves for a semester to return to the industry, this four-semester clock is stopped. The Ph.D. prospectus clock stops when the student leaves and resumes at the start of the semester when the student returns. Students who have not met their Ph.D. prospectus requirement within the four-semester time limit must discuss a revised timeline with their advisor prior to the semester’s Graduate Progress Review faculty meeting.

Ph.D. Dissertation Defense

Once the Ph.D. dissertation is written, the student must submit the Defense Declaration form to the Graduate Affairs Office at least two weeks before the student’s defense date. The Thesis Committee is usually the same as the Prospectus Committee. If there is any change in the committee, the student must submit a biographical description of any new committee member from outside CMKL for approval. Additionally, students are expected to adhere to AiCE guidelines on providing defense committees with a copy of the thesis prior to the defense. AiCE recommends that students provide their committee with a copy of the thesis at least one and a half months prior to the defense.


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