# From Kalmár to LLMs, University of Szeged cements AI leadership with new MSc and 1.75 petaflops supercomputer

> University of Szeged boosts AI: English-language MSc begins Sept 2026, backed by a 1.75-petaflop supercomputer. Work spotlights generative models and early disease risk.

- Type: AI news
- Page: https://completeaitraining.com/news/from-kalmar-to-llms-university-of-szeged-cements-ai/
- Published: 2026-03-07
- Updated: 2026-03-08
- Author: Jeroen Erne
- Jobs: Science and research
- Reading time: 2 min

### University of Szeged Strengthens Its Key Position in AI Research

The University of Szeged (SZTE) is doubling down on AI. Starting in **September 2026**, the university will offer an English-language **MSc in Artificial Intelligence** built for serious research and high-impact applications.

This move builds on a long tradition. AI research in Szeged dates back to the 1950s, when Professor László Kalmár's lab developed one of Hungary's earliest experimental "robots," Katica. That foundation now supports a full stack of research, education, and innovation run through the Institute of Informatics and the Artificial Intelligence Competence Center, led by Professor Márk Jelasity.

#### Infrastructure that lets research move faster

SZTE introduced an AI-optimized supercomputer in the summer of 2025 with **1\.75 petaflops** of compute, built on [Hewlett Packard Enterprise technology](https://www.hpe.com/us/en/compute/hpc.html?ref=completeaitraining.com). It's available university-wide and built to support training, evaluation, and large-scale experimentation across projects.

#### Research focus: generative models and health

One flagship initiative, **RAItHMA**, studies how generative models (including large language models) behave in real settings-covering reliability, safety, and practical deployment. For researchers tracking LLM methods and applications, see [Generative AI and LLM](https://completeaitraining.com/tag/generative-ai-and-llm/).

The university is also putting its extensive health data resources to work. Teams are building AI-driven predictive models to estimate disease risk-such as cancer-up to a decade in advance. This includes rigorous attention to validation, clinical relevance, and the legal and ethical dimensions of AI in healthcare.

#### Graduate programs that feed research and industry

The new **MSc in Artificial Intelligence (from Sept 2026)** will cover the foundations and practice of AI: machine learning, generative AI, data processing, and AI law and ethics. It offers two specializations:

- **AI Researcher** \- for students aiming at academic or industrial research, with a focus on theory, methods, and publishing.
- **AI Engineer** \- for building and shipping AI systems in software and product contexts, with emphasis on engineering and integration.

In 2027, a new **Master's in Electrical Engineering** will launch with three tracks: embedded systems, space technology, and artificial intelligence. It's unique in Hungary's Southern Great Plain region and spans hardware, software, and AI-useful for cross-disciplinary R&D.

#### Why this matters for scientists and research teams

- Access to serious compute for training and evaluation without leaving campus.
- Focused work on generative model reliability, reproducibility, and deployment.
- Health AI projects grounded in real data and measurable outcomes.
- A clear talent pipeline via MSc tracks aligned with research labs and industry needs.
- Collaboration across informatics, engineering, and domain sciences-shortening the gap from idea to experiment.

If you're building research programs or upskilling teams, explore [AI for Science & Research](https://completeaitraining.com/tag/science-and-research/) for methods, tools, and training resources.

#### Leadership's stance

Professor Márk Jelasity highlights the supercomputer's role in supporting a growing portfolio of AI work across the university. Vice-Rector for Scientific Affairs and Innovation, Professor Zoltán Kónya, emphasizes a clear strategy: treat AI as a connected ecosystem-where education, research, and societal impact reinforce each other.

Szeged's direction is straightforward: build the infrastructure, run ambitious research, and educate people who can push AI forward-in labs, clinics, and industry.

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