Student working on technology

Artificial Intelligence

Doctor of Philosophy (Ph.D.)

Unlock the future of innovation and tackle society’s most pressing AI challenges through advanced study in artificial intelligence.

Become a leading AI expert through our Artificial Intelligence Ph.D. program. Go beyond the basics, blending theoretical foundations with practical applications in machine learning, robotics, ethics, and more.

Program type:
Doctoral Degree
Format:
On Campus
Est. time to complete:
4-5 years
Credit hours:
90

Why earn a Ph.D. in Artificial Intelligence?

Application Deadlines
Fall:
May 1
Spring:
Oct. 1

If you're an international student, refer to the international application process for deadlines.

In this program, you’ll engage in interdisciplinary collaboration with leading experts across diverse fields such as computer science, data analytics, biomedical sciences, law, psychology and engineering. This unique approach will allow you to tailor your studies to align with your specific research interests, equipping you with the knowledge and skills to address complex challenges in both theoretical and applied AI. You’ll explore cutting-edge topics like machine learning, neural networks, ethical AI and autonomous systems, creating a curriculum that supports your long-term goals. 

Through completion of the Artificial Intelligence Ph.D., you will: 

  • Tailor your coursework and research to align with your career goals, exploring interdisciplinary applications in areas like healthcare, law, engineering and more.
  • Collaborate on groundbreaking projects and contribute to solving real-world challenges through extensive research, supported by mentorship from leading experts.
  • Develop the technical analytical and problem-solving skills necessary for high-demand roles in academia, industry and government.
  • Build connections with faculty, peers and industry leaders, opening doors to internships, collaborations and career opportunities in AI and related fields.
  • Stay ahead in a rapidly advancing field with a program designed to adapt alongside industry trends and emerging technologies.

Curriculum

UND’s Ph.D. in Artificial Intelligence requires you to complete core courses, electives, seminars and dissertation research. The core curriculum focuses on artificial intelligence, machine learning and data science. Choose from elective courses grouped into four areas of specialization:

  • AI Foundations: Covers cognitive psychology, communication technology and algorithm analysis.
  • Advanced AI Techniques: Focuses on machine learning, computational intelligence and data visualization.
  • Machine Vision and Robotics: Includes autonomous systems, machine vision and robotics.
  • AI Applications: Explores bioinformatics, quantum chemistry and behavioral data analytics.

UND's Artificial Intelligence Ph.D.

  • Gain a comprehensive understanding of both computational theory and the human impacts of AI, preparing you to tackle complex problems from multiple perspectives.

  • Explore a wide range of topics and research areas, allowing you to apply AI in innovative ways across various industries such as healthcare, finance and engineering.

  • Contribute to AI-related education and service opportunities, giving back to the university and local communities while making a meaningful impact. 

  • Benefit from small class sizes and close-knit research groups that encourage 1:1 mentorship with faculty, ensuring individualized guidance and support throughout your studies.

  • Stay ahead of industry trends with a curriculum and research projects that are continuously updated to reflect the latest advancements in the rapidly changing AI field.

  • Study at a Carnegie R1 Institution ranked #151 by the NSF. Students are an integral part of UND research.

Ph.D. in Artificial Intelligence Jobs

357k

Job openings projected each year in AI fields for the next decade (much faster growth than average of all occupations)

U.S. Bureau of Labor Statistics

115k

Average base salary of occupations requiring a Ph.D. in Artificial Intelligence

Payscale

A Ph.D. in Artificial Intelligence will offer you a variety of fulfilling career paths across tech, business and academia. The demand for skilled professionals in these areas is expected to continue rising as AI technologies become more integrated into daily life and business processes; potential careers include: 

  • AI Researcher - Study and develop cutting-edge AI technologies, pushing the boundaries of what AI can do. Work on both theoretical and practical applications, exploring new methods and algorithms to advance the field.
  • AI Engineer - Develop and program advanced AI algorithms and systems, creating neural networks that allow AI to learn and perform tasks like humans. Work with machine learning and deep learning techniques to build applications that solve complex problems in industries such as healthcare, finance and autonomous vehicles.
  • Data Scientist - Find patterns and trends in datasets to uncover insights. Create algorithms and data models to forecast outcomes and use machine learning techniques to improve the quality of data or product offerings.
  • Machine Learning Engineer - Design, build and maintain software that uses artificial intelligence to analyze data and perform tasks such as data management, model development, system development and communication.
  • AI Ethics Specialist - Play a crucial role in guiding the responsible development and implementation of artificial intelligence technologies. Analyze and address the ethical implications of AI systems, ensuring that these technologies are designed and used in ways that respect human rights, fairness and diversity.
  • AI Policy Expert – Analyze the societal impacts of artificial intelligence and work to develop policies that ensure its ethical and responsible use. Focus on creating frameworks that address issues such as privacy, security and fairness, helping guide the development and implementation of AI technologies in a way that benefits society. 

Companies Hiring Graduates with a Ph.D. in Artificial Intelligence

  • Google
  • LinkedIn
  • Microsoft
  • Amazon
  • Apple
  • IBM
  • NVIDIA
  • Tesla
  • Salesforce
  • Meta
  • Adobe 

Artificial Intelligence Ph.D. Courses

DATA 532. Applied Machine Learning. 3 Credits.

Implementation and application of common machine learning algorithms using a high-level programming language. Algorithms that employ supervised and unsupervised learning will be considered in the context of a variety of applications such as searching and ranking, text mining, and recommender systems. Prerequisite: DATA 511, DATA 512, and DATA 513 or permission of the School of Electrical Engineering and Computer Science. On demand.

EFR 530. Learning Analytics. 3 Credits.

Learning analytics is the collection, management, analysis, and reporting of meaningful patterns in data about learners, aimed at optimizing learning and the environments in which it occurs. This course will provide students with the building blocks of learning analytics, including history, concepts and theories, question development, common data sources, tools and techniques, challenges, ethics, applications, case studies, and presenting to educational audiences for decision-making. F, even years.

CSCI 543. Machine Learning. 3 Credits.

An introductory course in machine learning for data science. Topics include the learning algorithms of a Bayesian network, neural network, parametric/non-parametric methods, kernel machine, support-vector machine, etc. for regression, classification, clustering, dimensionality reduction, etc. Prerequisite: CSCI 365 or CSCI 384. F, odd years.

DATA 525. Data Engineering and Mining. 3 Credits.

This course studies theoretical and applied issues related to data engineering and mining. Data engineering is to identify, investigate, and analyze the underlying principles in the design and effective use of information systems; and data mining is to discover patterns in large data sets and transform the patterns into a comprehensible structure for further applications. The following topics are covered: data collection, data preparation, data indexing and storage, data processing and analysis, data classification and clustering, knowledge discovery, information retrieval, data visualization, data sharing, data applications, and some other special topics. Prerequisite: DATA 511, DATA 512, and DATA 513 or permission of the School of Electrical Engineering and Computer Science. On demand.

EE 752. Introduction to Autonomous Systems. 3 Credits.

Advanced topics in autonomous and intelligent mobile robots, with emphasis on planning algorithms and cooperative control. Robot kinematics, path and motion planning, formation strategies, cooperative rules and behaviors. The application of cooperative control spans from natural phenomena of groupings such as fish schools, bird flocks, deer herds, to engineering systems such as mobile sensing networks, vehicle platoon. Prerequisite: Consent of the instructor. On demand.

CSCI 575. Analysis of Algorithms. 3 Credits.

The time and space complexity of classical computer algorithms is analyzed. NP hard and NP complete problems are characterized and illustrated. Prerequisite: CSCI 435.

Explore More Options

Connect with the faculty you'll work with at UND or discover additional education opportunities.

Department Contact
Emanuel Grant
Graduate Director
P 701.777.4133

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