Three Hungarian Research Teams Selected in Joint MIT–HUN-REN Call

28.04.2026

Three research teams have been awarded funding under the joint call launched by the Massachusetts Institute of Technology (MIT) and the HUN-REN Hungarian Research Network (HUN-REN). This marks the first time that HUN-REN researchers have participated in MIT’s Global Seed Funds (GSF) programme through a dedicated joint MIT–HUN-REN funding scheme. The collaboration is particularly significant in international terms: the MIT GSF programme includes partner institutions only in 23 countries worldwide, 10 of them in Europe.

The mission of HUN-REN is to strengthen Hungary’s innovation-driven economic growth and international scientific recognition through world-class basic and applied research. In line with this mission, HUN-REN’s leadership has actively sought opportunities in recent years to advance these objectives. As part of this effort, HUN-REN entered into a strategic co-operation agreement with MIT—widely regarded as the world’s leading technical university—to participate in the Global Seed Funds programme operated by MIT’s Center for International Studies (CIS).

Under this agreement, the parties established the MIT-Hungary HUN-REN Seed Fund, which supports joint research groups led by MIT and HUN-REN researchers and enables Hungarian researchers and students to participate in the programme. The partnership is distinctive on the international stage: Hungary is one of 23 participating countries in the MIT GSF programme, including 10 in Europe.

In the first call cycle, which closed in December 2025, MIT faculty and HUN-REN researchers submitted six joint proposals. Following an evaluation process involving both HUN-REN and MIT, MIT’s review committee recently selected the projects to receive funding. The list of successful proposals is also available on MIT’s website.

The three winning projects are:

The origins of the “light” heavy elements (HUN-REN Research Centre for Astronomy and Earth Sciences); lead researchers: Maria Lugaro, Research Professor, and Marco Pignatari, Senior Research Fellow; MIT partner: Anna Frebel, Professor of Physics, Division Head for Astrophysics.
Autonomous control of advanced reactors (HUN-REN Centre for Energy Research); lead researchers: András Szabolcs Ványi and Zoltán István Böröczki; MIT partners: Sacit Cetiner, Scientific Director of the Nuclear Reactor Laboratory, and Benoit Forget, Department Head of Nuclear Science and Engineering.
On timing constraints of XR applications introduced by AI pipelines (HUN-REN–BME Cloud Applications Research Group); lead researcher: László Toka, Professor, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics; MIT partner: Fox Harrell, Professor of Digital Media and Artificial Intelligence at the Institute for Data, Systems, and Society.

Through the MIT-Hungary HUN-REN Seed Fund, HUN-REN researchers gain access to a funding framework that supports the earliest stages of international scientific collaboration. The programme aims to connect MIT faculty, researchers, and students with researchers and research groups from HUN-REN institutions in joint, pioneering research projects. These collaborations are expected to develop into long-term, large-scale research partnerships between MIT and Hungary’s largest research network, fostering scientific excellence and innovation.

The initial three-year funding period, beginning with the 2025/2026 call cycle, is financed by HUN-REN’s newly established technology transfer company, HUNRENTECH. Each year, several projects may be supported. The MIT-Hungary HUN-REN Seed Fund provides a total of USD 225,000 over three years, primarily to support travel, workshops, and joint research activities. Project durations are up to 20 months. The next call is expected to open in September 2026.

Further information on the funded projects, participating researchers, and the MIT Global Seed Funds programme is available in the attached document.

BACKGROUND INFORMATION

ON THE MIT–HUNGARY HUN-REN SEED FUND AND THE FUNDED PROJECTS

MIT Global Seed Funds

The Global Seed Funds (GSF) programme is an initiative of the Center for International Studies (CIS) at the Massachusetts Institute of Technology (MIT). It supports collaboration between MIT faculty and leading researchers working in higher education institutions, research institutes, and research networks across its current 23 partner countries, with the aim of addressing complex global challenges.

The GSF consists of several funding streams. Some are dedicated to collaborations with specific countries, regions, or institutions, while a general fund is also available for projects in any country. Seed fund programmes primarily support in-person collaboration, including travel and workshop-related costs for joint research activities.

Further information: Global Seed Funds | Center for International Studies

Funded Projects

The Origins of “Light” Heavy Elements

Title: The puzzling origins of the “light” heavy elements: the mass region between Sr (Z=38) and Pd (Z=46)

Project description:

For decades, astronomers have confidently explained the origin of the heaviest elements in the Universe such as barium, gold, lead, and uranium. These are primarily created inside stars through two mechanisms: the slow (s-process) and rapid (r-process) neutron-capture nucleosynthesis processes, when seed nuclei capture neutrons to build up heavier elements. However, a group of somewhat lighter elements — strontium, yttrium, zirconium, niobium, molybdenum, ruthenium, silver, and palladium — remains poorly understood. Thanks to modern telescopes, we can now measure these elements in thousands of stars, including very ancient, metal-poor stars that formed just a few hundred million years after the Big Bang. Despite this wealth of data, researchers still disagree on which stars produced them. Possible sources include hot neutrino-driven winds in massive core-collapse supernova explosions, the outer layers of Sun-like stars in their last evolutionary phase, and extremely fast-rotating massive stars.

This project aims at solving this mystery by combining the world’s largest and best-characterized samples of ancient stars collected and curated by MIT Professor Anna Frebel with the most comprehensive library of stellar nucleosynthesis simulations, covering a wide range of possible stellar sources, developed at Konkoly Observatory at HUN-REN Research Centre for Astronomy and Earth Sciences by Prof. Maria Lugaro and Prof. Marco Pignatari. By systematically comparing observational results with models, they will determine which processes dominated the production of these elements in the early Universe and clarify their role in the chemical evolution of the Milky Way.

HUN-REN Researchers Participating in the Project

Maria Lugaro (https://konkoly.hu/en/staff-members/lugaro-maria)

Maria Lugaro is an Italian-born astrophysicist who has been research professor at the Konkoly Thege Miklós Astronomical Institute of the HUN-REN Research Centre for Astronomy and Earth Sciences since 2014. After completing her studies at the University of Turin, she obtained her PhD from Monash University in Australia. Her main research areas include nuclear reactions in stars, neutron-capture processes in giant stars, the analysis of meteoritic stardust, and the galactic origin of radioactive nuclei. She was awarded funding from the Hungarian Academy of Sciences’ Momentum (Lendület) Programme in 2014 and 2023, and received an ERC Consolidator Grant in 2016, under which the RADIOSTAR project was launched in 2017.

Marco Pignatari (https://konkoly.hu/munkatarsak/pignatari-marco)

Marco Pignatari is an Italian astrophysicist who has been a senior research fellow at the Konkoly Thege Miklós Astronomical Institute since 2021. He studied at the University of Turin, where he earned his PhD in astrophysics in 2006. Following extensive international experience—including a position as a reader at the University of Hull—he moved to Hungary to join the HUN-REN Research Centre for Astronomy and Earth Sciences. His main research areas include the formation of elements and isotopes in stars and supernovae, the study of presolar grains, and the chemical evolution of the Milky Way. As a leader of international collaborations, he plays a key role in nuclear astrophysics and stellar nucleosynthesis modelling.

Autonomous Control of Advanced Reactors

Title: Autonomous control of advanced reactors for staffing reduction

Project description:

The operation of conventional nuclear power plants is characterized by a significant labor cost per unit of electricity generated compared to natural-gas-fired power plants. A substantial portion of these costs is attributed to reactor operators stationed in the main control room. While large nuclear power plants can absorb these costs, the relative impact is substantial for small-modular reactors (SMRs) and, particularly, smaller-sized portable reactors commonly referred to as microreactors or nuclear batteries.

Autonomous operation is a comprehensive hierarchical control engineering framework with the ultimate goal of significantly reducing staffing levels for nuclear power generation systems. To achieve the overarching objectives of autonomy for nuclear power plants, it is critical that the phenomena related to reactor dynamics be characterized in detail. Specifically, knowledge on spatial power distribution in the core, and how that affects transient response is imperative.

This project aims to establish a path for future collaboration between MIT and HUN-REN on reactor dynamics. To that end, MIT will share expertise in OpenMC for transients, which is currently under development. Additionally, MIT will also train the students from Hungary on the utilization of the MIT Graphite Exponential Pile (MGEP). Likewise, the HUN-REN Center for Energy Research (CER) and the Budapest University of Technology and Economics (BME) will offer lectures on experiment design and applicable procedures in the 10-MW Budapest Research Reactor (BRR), and the 100-kW Training Reactor (TR), respectively.

Hungarian participants in the project:

András Szabolcs Ványi (https://www.ek.hun-ren.hu/futoelem-es-reaktoranyagok-laboratorium)

András Szabolcs Ványi is a reactor physicist and researcher at the HUN-REN Centre for Energy Research and the Budapest University of Technology and Economics (BME). He completed his studies and obtained his PhD at BME, where his doctoral research focused on the multiphysics experimental and numerical analysis of the BME Training Reactor. His research activities include reactor physics measurements, the modelling of transient phenomena, and the validation of multiphysics codes. He has gained international experience at the École Polytechnique Fédérale de Lausanne (EPFL) and the OECD Nuclear Energy Agency, and his work was recognized with the Fermi Young Researcher Award in 2024.

Zoltán István Böröczki (https://www.ek.hun-ren.hu/nuklearis-biztonsagi-laboratorium/)

Zoltán István Böröczki is a researcher specializing in reactor physics and nuclear safety at the Nuclear Safety Laboratory of the HUN-REN Centre for Energy Research. He obtained his PhD in 2023 from the Doctoral School of Physical Sciences at the Budapest University of Technology and Economics (BME), where his doctoral research focused on the neutronic analysis of demonstration and experimental fast reactors. His research activities include steady-state and transient reactor physics calculations, sensitivity and uncertainty analysis, and the validation of methods used in safety analyses. His work contributes to the reliable and safe design of next-generation nuclear reactors.

The Impact of Latency in AI-Supported Extended Reality (XR) Applications

Title: On timing constraints of XR applications introduced by AI pipelines

Project description:

Advances in Extended Reality (XR), Artificial Intelligence (AI), and cloud computing are creating new opportunities for interactive training and skill development. In sports such as fencing where timing, precision, and rapid decision-making are essential, these technologies can offer athletes real-time guidance, strategy insights, and immersive practice environments. However, delivering AI-generated feedback or dynamic XR content quickly enough is challenging. Even small delays in network communication or cloud-based processing can interrupt the flow of training, reduce the sense of immersion, or impact how effectively an athlete can respond during practice. This project investigates how such timing delays influence user experience and performance in AI-supported XR training systems. Fencing provides an ideal testbed because it requires split-second reactions and continuous interpretation of an opponent’s movements.

By analysing how athletes perceive and adapt to delays in AI feedback, motion tracking, or XR visual updates, the project will map the tolerance levels for different types of latency and identify when delays begin to hinder learning, focus, or tactical creativity. The team will also explore future training possibilities enabled by reduced latency, such as ultra-responsive virtual sparring partners, cloud-assisted motion analysis, or adaptive coaching tools that adjust instantly to an athlete’s actions. The ultimate goal is to develop guidelines for designing low-latency XR-AI systems that support more fluid, engaging, and effective training experiences. Although demonstrated through fencing, the insights gained will benefit many fields where real-time XR and AI interaction is essential, from education to rehabilitation and beyond.

Hungarian participant in the project:

László Toka (https://www.tmit.bme.hu/toka.laszlo)

László Toka is a computer scientist and researcher specializing in network and cloud systems. He is a Doctor of the Hungarian Academy of Sciences (DSc) and a Professor at the Faculty of Electrical Engineering and Informatics of the Budapest University of Technology and Economics (BME), where he teaches at the Department of Telecommunications and Artificial Intelligence.

His research focuses on cloud and edge computing systems, network resource management, service outsourcing, and the performance, reliability, and economic modelling of these systems. In these areas, he is an active member of the HUN-REN–BME Cloud Applications Research Group, which investigates cloud-based applications for 5G/B5G/6G networks within the framework of collaboration between BME and HUN-REN.

His scientific work is closely related to the mathematical and game-theoretical analysis of network systems, with a particular focus on resource allocation and pricing problems in cloud environments.