COMPUTER ENGINEERING

The Computer Engineering program has become a fundamental engineering field that expands the scope of computer engineering today beyond merely software development and IT infrastructures, directing it towards the modeling and analysis of complex systems and the design of decision support mechanisms. Large-scale data processing, machine learning, numerical modeling, high-performance computing, and algorithmic optimization are now positioned as integral components of modern computer engineering. The use of computation-based methods in a very wide spectrum, ranging from physical systems to social infrastructures, and from climate models to biological processes, has turned this field into a strategic engineering discipline.

Artificial intelligence and high-performance computing centers supported by the National Science Foundation (NSF) and the Department of Energy (DOE) in the USA, and advanced computing infrastructures established under the EuroHPC Joint Undertaking and Digital Europe Programme in Europe, clearly demonstrate that artificial intelligence and computational sciences are being treated as an independent and holistic area of expertise within computer engineering. In line with this global trend, the Computer Engineering program aims to address data, algorithms, and computational infrastructures by integrating them within a single engineering framework.

In the program, artificial intelligence, machine learning, and data science are not merely treated as abstract algorithms or solely at the application level; they are evaluated in conjunction with a strong mathematical foundation, numerical methods, and computational architectures. Computational modeling, simulation, and large-scale optimization techniques are among the distinctive elements of the program, which aims to reliably apply artificial intelligence solutions to physical systems, critical infrastructures, and complex socio-technical structures. Furthermore, cybersecurity is not considered as a separate area of expertise, but rather as a fundamental engineering component embedded within the processes of data processing, algorithm design, and system architecture.

The primary goal of the Computer Engineering program is to provide an education that integrates algorithmic thinking, computational modeling, and artificial intelligence development skills with the core knowledge areas of computer engineering. Within this intensive, and application-oriented 7-semester (3.5-year) curriculum, students acquire the skills to formulate problems correctly, interpret data, build numerical and algorithmic models, run these models on high-performance computing infrastructures, and transform the results into decision support systems.

The program aims to train its graduates to be computer engineers who not only develop software but also can design data, algorithm, and computational infrastructures together. Reliability, scalability, explainability, and sustainability are considered as fundamental design principles for the artificial intelligence and computational systems developed. In line with this vision, graduates possess technical depth as well as an engineering perspective that considers real-world constraints ( performance, security, ethics, regulation, and cost ).

Digitalization is no longer a transformation process limited to specific sectors; it has become a fundamental infrastructure layer that determines the operational logic of all engineering and production systems. Artificial intelligence and computational sciences play a decisive role in a wide range of areas, from public services to industry, finance, energy, and transportation infrastructure, through prediction, optimization, automation, and decision support mechanisms. Therefore, data and computational engineering capabilities stand out as a strategic element that directly affects the competitiveness of organizations, the efficiency of public systems, and the resilience of societies.

In the future, artificial intelligence will become widespread not only in the form of software products, but also as an integral component of engineering systems. Digital twins, autonomous systems, large-scale simulations, climate and disaster risk modeling, and the use of artificial intelligence in scientific discovery processes (AI for Science) will be the main areas accelerating this trend. The seven-semester (3.5-year) structure of the program offers an agile and adaptable educational model that can adapt to this rapid transformation, guiding students towards application and project production at an early stage.

Graduates of this program have a wide range of employment opportunities as experts who can combine artificial intelligence and computational methods with a foundation in computer engineering. Software and technology companies, R&D centers, the defense and security ecosystem, energy and transportation infrastructures, the finance and insurance sector, and public institutions are among the prominent areas of employment for graduates.

Graduates can work in roles such as data and algorithm engineers, artificial intelligence developers, computational modeling and simulation specialists, large-scale systems architects, and decision support systems developers. Furthermore, their strong mathematical and computational backgrounds enable them to directly transition to interdisciplinary master's and doctoral programs. Within the entrepreneurial ecosystem, they can take active roles in technology development, productization, and the commercialization of AI-based solutions.

The program is structured with an intensive, 7-semester (3.5-year) application-based approach to computer engineering education. Core courses such as mathematics, algorithms, and data structures are supported by project studios and lab work focused on artificial intelligence, machine learning, and computational sciences. From an early stage, students gain experience developing end-to-end engineering solutions by working with real datasets and computational infrastructures.

Two mandatory summer semesters are structured around industry internships, research projects, or entrepreneurial activities, ensuring students forge strong ties between academia and the business world. This educational approach aims to equip graduates not only with technical knowledge but also with essential components of computer engineering practice, such as problem definition, critical thinking, ethical responsibility, communication, and project management.