Welcome to Ajman University’s Doctor of Philosophy in Artificial Intelligence (PhD-AI), a research-intensive doctoral program designed for ambitious scholars who want to shape and lead the future of intelligent systems and transformative technologies. Offered through the College of Engineering and Information Technology, this program builds on Ajman University’s established AI ecosystem which includes a suite of AI programs (BSc in AI, BSc in Data Analytics, and MSc in AI, and provides a high-impact environment for advanced study, innovation, and publication-driven research.
At Ajman University, doctoral candidates join a unique academic community supported by internationally experienced faculty, a dedicated Department for Data Science and Artificial Intelligence, advanced AI laboratories, and the well-established Artificial Intelligence Research Center. The program is aligned with the UAE Artificial Intelligence Strategy 2031 and is designed to prepare graduates who can contribute meaningfully to academia, industry, government, and society through original AI research and innovation.
The PhD in Artificial Intelligence (PhD-AI) at Ajman University is a research-intensive doctoral program that prepares future leaders in intelligent systems, scientific discovery, and technological innovation. The program is offered by the UAE’s top-ranked university (top 100 universities worldwide) in Data Science and Artificial Intelligence, and is supported by faculty recognized among the world’s top scientists.
The program offers a structured doctoral journey that combines:
Built upon Ajman University’s established BSc and MSc programs in AI and Data Analytics, the PhD completes a comprehensive academic pathway within a mature AI ecosystem rather than a developmental program. It is designed for scholars seeking advanced training, strong supervision, and meaningful contributions in areas such as healthcare innovation, smart cities, sustainability, cybersecurity, financial technologies, digital transformation, multimodal AI, and embedded intelligence.
The PhD in AI aims to:
Contribute to the responsible and ethical development of AI systems aligned with societal needs and national priorities.
Prof. Mohamed Deriche (Profile)
Applicants seeking admission to the PhD in Artificial Intelligence must satisfy the University’s doctoral admission requirements. In general, applicants should have:
Conditional Admission
Conditional admission may be granted in specific cases, including:
Such applicants must satisfy all outstanding conditions by the end of the first semester.
Transfer Admission
Transferred students may be considered subject to University policy. A maximum of 15 credit hours may be transferred, while thesis/dissertation transfer is not permitted.
Program Duration
The PhD in Artificial Intelligence is a 54-credit-hour program that can be completed in as few as 3 years in full-time mode, with an expected duration of 3 to 5 years depending on student progression and research milestones. The program is offered full-time, face-to-face.
Scholarships
Ajman University attracts the best and brightest students from across the region. For students with the potential to thrive, the university provides a number of scholarships options. Details can be obtained from the program coordinator.
To graduate from the PhD in Artificial Intelligence program, students are expected to complete all program requirements, including:
The PhD in Artificial Intelligence at Ajman University is competitively priced in alignment with leading PhD programs in Artificial Intelligence, Computer Science, and Engineering across the UAE. The tuition fee is estimated at AED 4,500 per credit hour, offering excellent value for a research-intensive program supported by advanced facilities, expert faculty, and a strong innovation ecosystem.
Graduates of the PhD in Artificial Intelligence will be positioned for leadership and specialist roles across academia, research, industry, and government. Career pathways include:
Upon successful completion of the program, graduates will be able to:
Students select advanced electives from areas such as:
Semester 1
Semester 2
Semester 3
Semester 4
Semester 5
Semester 6
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Full-time |
Fall |
Spring |
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Year 1 |
DAI701 – Research Methodologies in AI DAI702 – Advanced Mathematical & Statistical Methods DAI704 – Advanced Artificial Intelligence |
DAI703 – Advanced Machine Learning DAI705 – Theoretical & Applied Computing Elective 1 DAI721 – PhD Research Seminar (0 CH) |
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Year 2 |
Elective 2 Elective 3 DAI723 – PhD Dissertation I Qualifying Exam |
Elective 4 DAI723 – PhD Dissertation I (Cont.) DAI723 – PhD Dissertation I (Cont.)
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Year 3 |
DAI724 – PhD Dissertation II |
DAI725 – PhD Dissertation III Thesis Final Defense |
This course equips PhD students with advanced research methodologies tailored to artificial intelligence, emphasizing the theoretical foundations, rigorous experimental design, and ethical conduct required for impactful AI research. It moves beyond basic applications to focus on formulating precise, testable questions; designing robust data collection and evaluation strategies; and addressing critical societal and ethical implications such as fairness, accountability, and reproducibility. Students develop advanced skills in both quantitative and qualitative methods, gain expertise in communicating research findings effectively, and are prepared to make original contributions to the global AI community. By integrating deep technical knowledge with interdisciplinary and responsible research practices, the course ensures that graduates are capable not just of applying AI methods, but of fundamentally advancing them through foundational, systematic, and societally aware research.
This course provides a comprehensive treatment of mathematical and statistical foundations for modern artificial intelligence and machine learning. It covers optimization theory, probabilistic modeling, statistical inference, and high-dimensional data analysis, with emphasis on theoretical understanding and research applications. Topics include convex and non-convex optimization, stochastic processes, Bayesian inference, information theory, and statistical learning theory. Students critically analyze state-of-the-art methodologies and develop mathematical tools necessary for designing and evaluating novel AI models. The course integrates theory with research-oriented applications, preparing students to formulate and solve complex problems in AI using advanced mathematical frameworks.
theoretical foundations, modern architectures, and emerging paradigms. Building on prior knowledge of supervised and unsupervised learning, the course explores statistical learning theory, advanced optimization techniques, deep learning foundations, and contemporary topics such as self-supervised learning, generative models, and multimodal learning. Emphasis is placed on critical evaluation of state-of-the-art methods, reproducibility, and the development of novel machine learning approaches. Students engage with recent research literature and undertake a research-driven project aimed at contributing to the advancement of the field.
The aim of this course is to provide graduate students with in-depth knowledge of AI principles, algorithms and techniques. Topics covered include Knowledge Representation schemes and Automated Reasoning, uncertain knowledge and probabilistic reasoning, search strategies, intelligent agents, machine learning, planning, and ethical and societal issues relating to artificial intelligence. Students also work in group projects (2 to 3 students).
This course establishes a rigorous and unified foundation in theoretical and applied computing for PhD students in Artificial Intelligence, ensuring a consistent baseline of computational knowledge and skills across diverse academic backgrounds. It covers core concepts in theoretical computer science, including discrete structures, algorithmic design and analysis, and fundamental data structures, alongside essential systems and programming competencies such as software development practices, computational efficiency, and basic operating system principles. Emphasis is placed on bridging theory and practice through problem-solving, implementation, and performance analysis, preparing students to engage effectively with advanced AI coursework and research. The course also highlights computational thinking, scalability, and reproducibility as foundational principles for modern AI systems.
This course offers an in-depth, research-focused study of advanced data mining methods and big data systems, integrating strong theoretical foundations with scalable architectures and modern AI-driven approaches to extract knowledge from large, diverse, and rapidly evolving datasets. It emphasizes algorithmic rigor and efficient distributed data processing techniques using modern big data tools, while addressing real-world challenges associated with large-scale data systems.
This course provides a comprehensive and in-depth study of advanced computer vision, covering both foundational principles and modern learning-based approaches. It begins with the fundamentals of image formation and image processing, including geometric and photometric modelling, filtering techniques, and frequency-domain analysis. The course then introduces core methodologies for model fitting and optimization, such as variational methods and probabilistic graphical models. Building on these foundations, the course explores deep learning techniques for vision, including supervised and unsupervised learning, convolutional neural networks, and advanced neural architectures, while considering model interpretability. Students will study key visual recognition tasks, including image classification, object detection, semantic segmentation, and video understanding, as well as multimodal vision-and-language models. In addition, the course covers feature detection and matching techniques, including interest points, edges, contours, and geometric structures, which are essential for many vision applications. Throughout the course, emphasis is placed on connecting theoretical concepts with practical applications in real-world vision systems. Furthermore, students will work on a course project, individually or in pairs.
This course provides an advanced exploration of intelligent robotic systems, emphasizing autonomous decision‑making, robust perception, and safe human‑centered interaction. PhD. students examine state‑of‑the‑art methodologies in belief‑space planning, LiDAR, and visual SLAM and semantic mapping. The course integrates modern frameworks for human–robot interaction, including cognitive intent modeling, multimodal human perception, shared autonomy, and safety‑critical control aligned with international certification standards. Through research‑driven lectures and a major project, students develop the analytical and experimental skills required to design, evaluate, and innovate next‑generation intelligent robotic platforms suitable for complex, dynamic, and human‑populated environments.
This course provides a rigorous, research-oriented examination of advanced statistical and deep learning paradigms for Natural Language Processing (NLP), with a focus on large-scale language models, pretraining strategies, and emerging architectures. Emphasis is placed on critical analysis of recent top-tier research (e.g., ACL, EMNLP, NeurIPS), including reproducibility, benchmarking, ablation studies, and failure analysis to assess robustness, limitations, and generalization. Students will engage in literature-driven inquiry to identify open research problems and design novel, well-justified solutions, culminating in a publication-quality research project grounded in rigorous experimental methodology. Ethical, societal, and reliability considerations in modern NLP systems are integrated throughout the course.
This doctoral-level course offers an in-depth exploration of advanced optimization methodologies and computational intelligence (CI) paradigms for solving high-dimensional, nonlinear, and multi-objective problems. The course bridges theoretical foundations with algorithmic innovations, encompassing both deterministic and stochastic optimization techniques. Students will study classical optimization methods (convex analysis, constrained programming, variational methods), alongside state-of-the-art CI approaches such as Genetic Algorithms, Particle Swarm Optimization, Differential Evolution, Ant Colony Optimization, Artificial Neural Networks, and hybrid metaheuristics. Emphasis is placed on algorithm design, convergence analysis, scalability, and application to complex systems in science and engineering. The course also integrates recent trends in hybrid intelligence, surrogate-assisted optimization, and real-world problem modeling, with applications in areas like machine learning, control systems, biomedical engineering, and supply chain optimization. Students will engage in critical analysis of algorithms, benchmark evaluation, and the development of novel CI approaches through a research-driven term project.
This course covers the fundamentals and advanced concepts of deep learning and generative artificial intelligence. It focuses on model design, training, and evaluation, including modern approaches such as transformers and diffusion models. Students will learn to build and analyze models using tools like PyTorch and TensorFlow, with an emphasis on both theory and practical applications.
The aim of this course is to provide students with fundamental knowledge of reinforcement learning principles, algorithms, and techniques for sequential decision-making problems. The course covers key concepts such as agents, environments, rewards, policies, and value functions. Topics include the mathematical foundations of reinforcement learning, Markov Decision Processes (MDPs), dynamic programming, Monte Carlo methods, temporal-difference learning, Q-learning, SARSA, and an introduction to Deep Q-Networks (DQN) and planning with tabular methods. Emphasis is placed on both theoretical understanding and practical implementation. Students also work on a course project individually or in pairs and present their results.
This course examines advanced AI techniques for designing intelligent biomedical and healthcare systems. It integrates machine learning, deep learning, multimodal data analytics, and digital health technologies to address complex clinical and healthcare challenges.
The course emphasizes research innovation, translational impact, and real-world deployment, preparing students to contribute to cutting-edge developments in AI-driven healthcare.
This seminar provides a doctoral-level platform for advanced research in Artificial Intelligence through structured scholarly engagement. Students critically evaluate cutting-edge literature, develop and refine dissertation research, and actively participate in peer-review processes.
The course emphasizes high-impact publication readiness, methodological rigor, interdisciplinary research thinking, and academic leadership, ensuring students transition from research consumers to independent knowledge producers.
Students must undertake and complete an independent theoretical and/or practical research under the supervision of a faculty member. Students are required to submit a dissertation proposal documenting their research idea and defend it in an oral examination before the proposal defense committee. This course guides students through the preparation and presentation of a comprehensive dissertation proposal in the field of Artificial Intelligence. Emphasis is placed on developing research questions, methodology, and a theoretical framework aligned with AI research standards. The outcome includes a formal research proposal approved by the committee and, if applicable, initial experimentation.
This course represents the primary implementation and data-gathering phase of the PhD journey. Building upon the proposal approved in Dissertation 1, students will focus on executing their research methodology. This includes the development and refinement of AI algorithms, large-scale data processing, and rigorous experimental validation. Students are expected to produce a significant portion of the dissertation’s core chapters, focusing on the implementation details and the analysis of preliminary results. Regular progress reviews with the supervisor ensure the research remains aligned with the field’s standards for originality and technical rigor.
This course guides students through the final stages of the PhD dissertation process, focusing on completing the full written dissertation, preparing for the oral defense, and addressing committee feedback. Students will produce a polished, submission-ready dissertation that demonstrates originality, rigor, and contribution to the field of Artificial Intelligence. The course emphasizes academic writing standards, integration of results, critical discussion, and professional defense skills.
The Qualifying Examination is an important step in the PhD journey that ensures students are well prepared to conduct advanced research in Artificial Intelligence and Machine Learning. It assesses students’ understanding of key concepts, theories, and methodologies, as well as their ability to think critically and solve complex problems. The exam may include written and oral components and may involve analysis of research topics and literature. Upon successful completion, students progress to the dissertation stage, where they focus on developing original and substantial contributions to a given field specialization.
What makes this PhD in AI distinctive?
The program is one of the few specialized PhD programs in AI in the region. It is research-intensive, aligned with national priorities, supported by advanced labs and the AIRC, and delivered by internationally experienced faculty with strong research records.
Is the program accredited?
Yes. The PhD in AI is accredited by the Commission for Academic Accreditation (CAA) of the UAE Ministry of Higher Education and Scientific Research, and it is delivered within Ajman University, an institution accredited by WSCUC.
How long does the program take?
The program can be completed in as few as three years in full-time mode, with a normal duration of 3–5 years depending on progress and dissertation milestones.
Who should apply?
The program is intended for graduates with a strong Master’s background in AI, Computer Science, Engineering, Mathematics, Data Science, or a closely related STEM discipline, as well as research-oriented professionals seeking advanced specialization and innovation leadership.
What research areas can students pursue?
Students may pursue doctoral research in areas such as machine learning, deep learning, generative AI, computer vision, natural language processing, optimization, intelligent robotics, biomedical AI, cybersecurity, smart cities, and sustainable intelligent systems.
Is there a dissertation requirement?
Yes. The program culminates in a 27-credit doctoral dissertation, including proposal development, research progression, and final defense.
Are scholarships or financial support available?
Applicants may consult Ajman University’s scholarships and financial aid services for available opportunities. Tuition and financial inquiry links are provided through the University.