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Syracuse University’s College of Engineering and Computer Science Launches AI Science Degrees

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Syracuse University’s College of Engineering and Computer Science will launch two new degree programs in artificial intelligence beginning in the fall of 2026: a Bachelor of Science and a Master of Science in Artificial Intelligence Science. The programs, offered through the Department of Electrical Engineering and Computer Science, are designed from the ground up for the way AI is built and deployed today.

The announcement comes as demand for AI talent continues to accelerate. U.S. job postings requiring AI skills grew 144 percent year over year as of April 2026, compared to 7 percent growth for all job postings combined, according to the Bipartisan Policy Center’s AI Skills Dashboard powered by Lightcast. LinkedIn ranked artificial intelligence engineer as the number one fastest-growing job title in the United States, with postings rising 143 percent in 2025. The Bureau of Labor Statistics projects employment of data scientists to grow 36 percent through 2033.

The United States faces a structural talent gap of 1.3 million AI job openings with current supply covering fewer than half, according to industry analysts. Workers with AI skills command wage premiums of up to 56 percent above peers in equivalent roles, according to PwC’s 2025 Global AI Jobs Barometer.

Bachelor’s program

The AI Science bachelor’s degree requires 120 credits and is structured across six components: a general education foundation, a computing core, a 15-credit AI core, a two-semester senior capstone, a concentration and upper-division electives. AI coursework begins in the second year — earlier than comparable programs — building toward specialization and internship opportunities before senior year.

Students choose between two nine-credit concentrations: a software concentration focused on algorithms, data and machine intelligence, or a hardware concentration in AI processor architecture, acceleration and silicon design. Syracuse says the program is among the first undergraduate AI degrees in the country to offer both a software and a hardware path.

The AI core covers five required courses: Technical Fundamentals of AI, AI Experiential Programming, Introduction to Machine Learning, Artificial Intelligence Algorithms and Natural Language Processing. The experiential programming course is built around using AI tools to build AI — students work with large language model-powered development environments, retrieval-augmented generation pipelines and AI agent frameworks such as Claude Code, Skills, and the Model Context Protocol.

A three-course senior capstone sequence connects students with regional industry partners. Past and expected collaborators include Lockheed Martin, Saab and Hidden Level, among other defense and technology employers in the Syracuse region.

Graduate program

The AI Science master’s program requires 30 credits and is designed to produce senior-level AI scientists and engineers capable of designing and analyzing AI systems across both software and hardware domains. Full-time students can complete the degree in three semesters; part-time students typically take four.

The program is structured around a 12-credit core, a three-credit specialization track, a six-credit capstone or thesis, six credits of AI electives and a three-credit engineering elective. The specialization tracks are AI hardware design and natural language processing.

Two 500-level bridge courses — Formal Foundations of AI and Introduction to the Theory and Practice of AI and Machine Learning — are designed to bring graduates from mathematics, physics and engineering fields into the program without requiring a computer science undergraduate degree or additional prerequisite coursework. The university says this removes a barrier that typically costs non-computer science applicants up to an additional year at other institutions – saving students time and tuition costs.

Students who pursue original research may elect the master’s thesis option, defended before a faculty panel under Graduate School standards. Students pursuing professional practice complete a two-semester capstone project in collaboration with external partners.

“This program was designed for students who have a strong technical foundation and want to lead AI projects, not just participate in them,” said Paulo Shakarian, director of the master’s program. “The bridge courses mean we can bring in physicists, mechanical engineers, mathematicians – and have them doing serious AI work in the first semester.”

AI science as a discipline

Employers are moving quickly. The BPC dashboard found that job postings mentioning AI skills grew at more than 20 times the rate of overall job postings in the year ending April 2026. Skills requirements in AI-exposed roles are changing 66 percent faster than in other fields, according to research from Index.dev.

Syracuse University’s programs differ from many AI offerings in their hardware emphasis. The bachelor’s hardware concentration and the master’s AI hardware design track cover FPGA acceleration, neuromorphic chip design, model optimization for silicon and systolic array architectures for deep neural network processing – areas the university says reflect growing demand from defense, robotics and embedded systems employers.

Syracuse University Launches New Minor in Artificial Intelligence Science and Engineering

A new minor in Artificial Intelligence Science and Engineering is designed to equip students with essential knowledge and skills in one of today’s most transformative fields. The minor will launch in the Fall 2026 semester.

New technologies such as Anthropic’s Claude and OpenAI’s ChatGPT are changing paradigms. The entire technology industry is pivoting toward the embrace of artificial intelligence. Coding agents are changing the way software is developed. Retrieval-augmented generation is changing the way companies manage data, and new systems promise further disruption. The new minor is designed to prepare students to thrive in this environment—providing them with skills highly sought after by employers in the age of AI.

The 18-credit program combines core computing principles with specialized AI coursework, preparing graduates to navigate and contribute to the rapidly evolving landscape of artificial intelligence. It can be easily paired with other STEM majors.

The minor requires completion of 18 credits divided into two components:

Computing Foundations (9 credits): Students build essential technical skills through coursework focused on computational disciplines, establishing the groundwork necessary for advanced AI study and providing the programming and mathematical basis to understand advanced concepts such as language models and supervised machine learning.

AI Fundamentals and Programming (9 credits): These courses delve into artificial intelligence concepts, methodologies, and applications, enabling students to develop expertise in this cutting-edge field. Courses include a strong focus on machine learning, using generative AI systems to create software, and understanding large language models for various applications such as retrieval-augmented generation.

This minor is offered through the College of Engineering and Computer Science and is open to all Syracuse University undergraduate students. It is designed for students seeking to enhance their primary degree with AI competencies.

Graduates of the program will possess key knowledge in artificial intelligence, positioning them competitively for careers in technology, research, data science, and emerging AI-driven industries. As organizations across sectors increasingly integrate AI into their operations, this minor provides students with highly sought-after qualifications.

For more information about admission requirements and course offerings, students should contact their academic advisor or Electrical Engineering and Computer Science Professor Priyantha Kumarawadu at spkumara@syr.edu.

The Importance of a Computer Science Education in the Era of Artificial Intelligence

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Alex K. Jones is the Klaus Schroder Endowed Professor and Chair of Electrical Engineering and Computer Science at Syracuse University. He previously served as a Program Director and Deputy Division Director at the National Science Foundation and was a professor for over two decades at the University of Pittsburgh. Since joining Syracuse in 2024, he has helped secure $4.5 million in research funding in AI hardware acceleration, semiconductor design, and workforce development. He is a Fellow of the IEEE Computer Society. He thanks Paulo Shakarian, the KG Tan Professor of AI at Syracuse University, for his comments on this piece.

For years, the formula seemed simple: get a computer science (CS) degree, walk into a high-paying job, and watch your career take off. It was the kind of promise parents could get behind and students could count on, but that story is changing. Uncertainty in the job market is partially to blame, but we also cannot ignore the rise of AI. The cumulative result has made it more difficult for new graduates to land the kinds of coding positions that used to feel almost guaranteed.

The hiring slowdown is real, and it is not just because companies are cutting budgets. Large language models and other AI tools can now do much of the work that used to go to entry-level programmers. In the past, the tech industry could absorb wave after wave of new CS grads because every product and service seemed to need more software. When that demand dipped, we could point to earlier disruptions like the switch from mainframes to personal computers, the outsourcing rush after the dot-com bust, and the rise of cloud computing and say, “We’ve been through this before.” But this time it feels different.  The disruption is arriving alongside something new: a flood of people who can code.

Over the past decade, universities have expanded CS programs at a record pace. Bootcamps promised to turn novices into developers in months. Coding became part of the standard toolkit for engineers, scientists, and researchers. That was good for broad technical literacy, but it also meant the job market was suddenly saturated with candidates with programming skills. Now that AI can write, debug, and optimize code on demand, that skill is no longer a differentiator. Yet the big companies are not finding that AI solutions are revealing a substantial cost savings.  Why?

Computer science has never just been about coding. AI itself is a product of computer science.  It is a discipline built on algorithms, data structures and the architecture of complex systems. People who understand these foundations are in a position not only to use AI tools but to improve them, adapt them, and build the next generation of breakthroughs. Computer science also intersects with other technical areas like quantum technologies, semiconductors, the Internet of Things, and wireless communications. Technologies succeed where theory, software and hardware meet, where computer science principles beyond coding are necessary, and the demand for talent is still strong.

Wireless communications is a great example, blending theory, protocols, and real-world system design. Fifth-generation wireless, or 5G, is an enabler of AI’s computing power in the cloud to connect instantly to the phone in your pocket. The wireless network is not just a backdrop for AI; it is the stage. Building it, securing it, and making it faster takes expertise that cannot be replaced with a few lines of generated code and is a place where job opportunities remain plentiful.

Deep domain knowledge can even make AI better. Some researchers are combining symbolic reasoning with machine learning to preserve complex concepts that traditional AI tends to lose. Others are adding “sanity checks” so systems can spot their own bad logic. And there are big, open problems on which only humans can lead the charge: reducing the energy footprint of large models, addressing bias in training data, and determining when and where AI should be trusted at all.

The problem is that current employers have created a false equivalency between computer science and coding. This is why the design of a CS program matters. Degrees that focus narrowly on coding risk leaving graduates exposed to job insecurity. A better approach is to combine a broad foundation across computing with the chance to go deep in a field where demand will last, like AI, wireless, cybersecurity, quantum computing and others. At Syracuse University, students have the full range of computer science at their fingertips with the opportunity to explore one of these high-impact areas. This breadth gives them flexibility. The depth gives them an edge.

A good degree program is not just a hedge against automation. It is a way to shape the future. The fundamentals of understanding context, solving problems, and thinking critically still determine who succeeds. “Just learn to code” is not a recipe for career security anymore. You can get that here at Syracuse, or almost anywhere in the country, if you want it. But, that’s not the CS degree we strive for or recommend to our students.  We teach the discipline, for sure, but that’s not our target differentiator. We want our graduates to see a pathway to success through skills like coupling their foundational knowledge with adaptability, continuous learning, and how to see opportunities where others see threats.

The value proposition for computer science has not vanished. It has evolved. It is not something coding bootcamps can replicate, nor is it something AI can replace.  A good CS degree does not just prepare students for the jobs of today, it provides them the skills to be ready for the jobs of tomorrow. A great CS degree prepares their graduates to design the jobs of tomorrow.