Soft Computing in the Computer Science doctoral program is a major focus on understanding and developing computational approaches that incorporate adaptive, flexible, and uncertainty-tolerant elements. Soft computing includes techniques such as neural networks, fuzzy logic, evolutionary computation, and probabilistic reasoning. Students in this discipline study the theory and application of these methods in solving difficult problems to achieve computationally sound solutions. They focus on developing models and algorithms that are capable of handling uncertain, ambiguous, or unstructured data. The main goal of this research group is to train researchers who can develop and apply effective algorithms and models in real-life problem-solving. Students are expected to have a deep understanding of the principles behind soft computing techniques and be able to integrate them with efficient and timely solutions.

Information Retrieval in Computer Science doctoral program focuses on developing methods, algorithms, and technologies to retrieve relevant information from large collections of scattered data or documents. It involves organizing, storing, searching, and analyzing data with the aim of presenting appropriate and meaningful information to users. Students in this discipline learn concepts and techniques such as indexing, text search, query management, sentiment analysis, information mining, natural language processing, and system performance evaluation. They work to understand how humans search for and process information, and how technology can help in optimizing this process. The main goal of this discipline group is to train researchers who can lead innovations in the Information Retrieval domain. This includes the development of smarter search engines, efficient indexing algorithms, and more intuitive user interfaces. Research in this group also includes how to improve precision and accuracy in finding relevant information, as well as understanding and overcoming challenges in handling big data.

Intelligent Systems in the Computer Science doctoral program addresses the development and application of systems that are able to learn, adapt, and make decisions autonomously through the use of artificial intelligence and advanced computational techniques. The main focus is on the development of algorithms, models, and technologies that enable computers to perform tasks that require high-level understanding, learning from data, and intelligent human interaction. Students in this group study a variety of areas, including machine learning, deep learning, reinforcement learning, natural language processing, computer vision, swarm intelligence, and evolutionary algorithms. They understand the fundamental principles behind artificial intelligence and apply this knowledge to build systems capable of making complex decisions. The main goal of this research group is to train researchers and experts who can develop artificial intelligence technologies that can solve complex real-world problems. This includes developing autonomous systems that can understand, learn, and adapt to their environment, as well as designing efficient algorithms for big and complex data analysis.

Mathematical Modeling in Computer Science doctoral program focuses on developing mathematical models that enable modeling and analysis of complex systems in various domains. The application of these mathematical models helps understand the behavior of real systems, predict outcomes, and plan appropriate actions. The main goal is to integrate mathematical and computational concepts to create models that are accurate and relevant in solving practical problems. The students in this group learn various mathematical techniques and computational algorithms that enable them to formulate and solve complex problems. They learn about model analysis, simulation, optimization, and verification to ensure their models are accurate and fit the needs of the application. The Mathematical Modeling discipline plays an important role in preparing researchers to face real-world challenges with a scientific and scalable approach. It also helps accelerate innovation and decision-making in a variety of fields, including technology, finance, environment, and health.

Advanced Networking in the Computer Science doctoral program is the primary focus for researchers interested in understanding, developing, and improving complex and advanced computer networks. The main objective of this discipline group is to explore the principles, protocols, architectures, and technologies associated with modern networks. Students in this group learn about the design and analysis of networks connected to cutting-edge technologies such as 5G networks, Internet of Things (IoT), Software-Defined Networking (SDN), Virtualization, Cloud Computing, and network security. They also study traffic management, network performance, reliability, and efficient routing schemes. In addition, the group delves into topics related to network security, including attack detection, data security, encryption, resource security, and privacy. Big data analysis is also an important part of research related to advanced networking.