Faculty of Artificial Intelligence

The Faculty of Artificial Intelligence at the European University of Science and Technology (EUST) educates learners in AI technologies—Machine Learning, Deep Learning, Big Data engineering and analytics—while building real‑world AI applications such as robotics, predictive systems, speech and face recognition, and autonomous systems. The Faculty delivers distinctive content using modern teaching strategies and expert academics to graduate professionals ready for top‑tier institutions.

To excel in AI technologies, systems, and applications that help achieve institutional and national goals, positioning the Faculty as an active global player in AI.

The Faculty is committed to graduating outstanding AI professionals with advanced skills and training on the latest technologies and management practices, ensuring their sustainability and competitiveness in the labor market and research at local, regional, and international levels—within a framework of professional and national values to serve society’s needs.

• Identify AI requirements and design/implement computer‑based systems that meet needs effectively, collaborating within teams to achieve shared goals.
• Apply AI and Computer Science knowledge to analyze problems and devise intelligent solutions.
• Demonstrate strong technical skills in programming, data handling, ML/DL, and model design.
• Work with robotics and control systems; apply AI in NLP, Computer Vision, Big Data, and advanced AI domains.
• Understand professional, ethical, security, and social responsibilities; communicate effectively; assess AI’s local and global impacts.
• Apply mathematical and CS foundations to design trade‑offs and software engineering principles for complex AI systems.
• Possess versatile capabilities to work across technology, research, industry, finance, healthcare, marketing, media, and more.

1. Prepare graduates capable of working across multiple AI domains and contributing to international conferences.
2. Develop AI researchers who advance AI technologies across sectors.
3. Build learner capacity in data engineering and analytics using sound scientific methods and advanced tools.
4. Enhance software engineers’ capabilities to develop AI‑powered software tools.
5. Provide scientific/technical consulting to entities adopting AI technologies.
6. Enable real‑time reporting and processing of large‑scale quantitative data for decision‑making (e.g., finance/banking).
7. Advance AI use in improving healthcare delivery in developing nations.
8. Qualify NLP practitioners to build chatbots, virtual assistants, predictive text, and other language tools.
9. Build applications that predict purchases, increase sales, and create customer‑centric e‑commerce experiences.
10. Align study programs with evolving labor‑market needs.
11. Reduce human error by training AI engineers to program systems correctly.

• Technology‑enhanced and competitive learning for higher mastery.
• Strong linkage between programs and current/future market demands.
• Teaching and training delivered by AI experts and specialists.
• Preparing students around emerging technologies to compete globally.
• Practical training guaranteed via agreements and MoUs with accredited international institutions.
• Flat Rate tuition for the first Faculty cohort until graduation.*
• Recognized certification at local, regional, and international levels.

The Faculty follows EUST’s credit‑hour system within an open framework that allows enrolled students to select elective courses alongside core requirements. All teaching and learning resources—written, audio, and video—are delivered via the University’s e‑learning platform.

• Artificial Intelligence Engineer
• Machine Learning Engineer
• Deep Learning Engineer
• Natural Language Processing Specialist
• Data Analyst / Data Scientist
• Robotics Engineer
• Big Data Engineer / Data Architect
• Web/Application Developer (AI‑enabled)
• Systems Analyst / Systems Specialist
• Database Developer / Administrator
• Support Engineer / System Administrator
• Software Engineer / QA / Technical Tester / Load Tester
• IT Auditor / Support Analyst
• Information Security Specialist / Network Security Engineer / Application Penetration Tester
• Cyber Risk Analyst
• Data Warehouse Architect / Data Center Supervisor
• ERP Developer / Medical Application Specialist
• IT Manager / Development Manager / System Manager
• Researcher (Academic/Industrial)