We are proud to announce that the Montenegrin Coinis, led by Tamara Pavlovic, has successfully secured resources through the EuroHPC JU Development Call to advance the development of their project AIChecker – an innovative platform for AI-generated text detection. The project has been awarded access to 4,500 node hours on the LUMI-G supercomputer, one of the most powerful computing systems in Europe, for a 12-month development phase starting in July 2025.
The team will focus on detection of AI generated text in digital media
Their project addresses a pressing global issue: the detection of AI-generated text in digital communication, education, and research. With the rapid rise of large language models (LLMs), the boundaries between human- and machine-written content are increasingly blurred. This project aims to safeguard writing integrity and foster the ethical use of generative AI technologies.
The project to be implemented during 12-month period using LUMI-G partition
As part of the ongoing EuroCC2 activities, representatives of the HPC National Competence Center (NCC) Montenegro, hosted at the University of Donja Gorica (UDG), visited the private medical institution Poliklinika Filipović in Podgorica. During the visit, the NCC team met with Dr. Verica Stanišić, a radiology specialist at the clinic, to discuss opportunities for collaboration in the application of High-Performance Computing (HPC) and Artificial Intelligence (AI) in medical diagnostics.
The purpose of the meeting was to introduce the clinic to the mission of EuroCC2, and to explore how state-of-the-art computational technologies can support the analysis of medical images, particularly in the context of early detection and prevention of breast cancer. The NCC Montenegro team shared insights into available national and European resources for HPC and AI, and highlighted use cases relevant to image analysis and clinical decision support.
Our host was dr Verica Stanisic
Our hosts presented the work of Poliklinika Filipović and demonstrated some of the latest imaging technologies they employ, including digital mammography, ultrasound, and elastography, which are crucial for early breast cancer detection and monitoring. These technologies are supported by a highly professional team and modern diagnostic infrastructure, forming a strong foundation for innovative clinical research and development.
The meeting concluded with a mutual interest in establishing future cooperation, including the joint exploration of pilot use cases in AI-assisted image interpretation and diagnostics. A follow-up meeting is already planned, where additional team members from both NCC Montenegro and Poliklinika Filipović will join the discussion to define next steps.
Another consultation meeting will be organized soon
This visit represents another important step in building cross-sectoral bridges between the HPC/AI community and healthcare professionals in Montenegro, paving the way for innovations that benefit both clinicians and patients.
The NCC Montenegro team at the University of Montenegro (UoM) regularly meets and collaborates with various research groups within the university. In this way, they stay updated on new research projects, emerging research directions, and especially the topics pursued by young researchers. As a result, the need for high-performance computing (HPC) resources has been identified for the master’s research conducted by two research assistants at the Faculty of Electrical Engineering, UoM, led by Assistant professor Miloš Brajović.
Their research deals with Graph Neural Networks (GNNs), with a particular focus on data representation, interpretability and scalability for complex scientific datasets. GNNs have demonstrated remarkable potential in modeling relational and structured data across various domains, including physics, chemistry, biology and computer vision. However, despite their predictive power, their “black-box” nature poses challenges in terms of explainability and trustworthiness, especially in critical applications such as scientific discovery and engineering.
Successful submission for EuroHPC call
To benchmarking state-of-the-art GNN architectures, evaluate their performance and scalability, and develop and test new GNN models and interpretability techniques for graph-based applications, these two researchers will require access to HPC resources. Therefore, the NCC Montenegro team supported them in preparing and submitting an application for the Development call to gain access to the Leonardo HPC. As a result, they got access to the Leonardo Booster partition, securing 4,500 node hours for their research.
Mr. Elvis Taruh successfully defended his master’s thesis titled “Development of Edge/AI Applications with HPC” at the Faculty of Information Systems and Technologies, University of Donja Gorica.
Mr Elvis Taruh
ABSTRACT – The efficiency of training artificial intelligence (AI) models has become a crucial factor in modern research, especially when dealing with complex systems that require substanial computational power. This study explores how the application of high-performance computing (HPC) and Edge devices can optimize the AI model training process, reducing processing time and improving efficiency. Through an experimental approach, AI model training was analyzed across three different platforms. Local computer, Google Colab and the HPC cluster at the University of Donja Gorica. As a practical example, livestock detection was used. By comparing the training time, memory consumption, and model accuracy, the research demonstrates that HPC clusters significantly accelerate the training process compared to traditional methods, while Edge devices enable faster real-time data analysis.
There was around 30 people attending. This was a small celebration for EuroCC2 and EuroCC4SEE projects
Mr. Arnad Lekić successfully defended his master’s thesis titled “Development of an AI Tutor Using Large Language Models and HPC” at the Faculty of Information Systems and Technologies, University of Donja Gorica.
Mr Arnad Lekic
ABSTRACT – This thesis explores the development of a personalized AI tutor using large language models (LLMs), with a specific focus on the LLaMA architecture and the application of High-Performance Computing (HPC) resources. The research involves the acquisition, setup, and evaluation of an open-source LLaMA model, with the goal of building a system capable of automated test grading. Special emphasis is placed on the training efficiency and feasibility of running the model locally using the available computing nodes, compared to cloud-based solutions like Google Colab. Beyond the technical implementation, the study also addresses the ethical challenges of using generative AI in education. Through experimental analysis, the research demonstrates that open models can be effectively adapted for educational purposes, with the potential to expand to grading diverse exam formats and generating educational content. The work provides directions for future development of systems leveraging advanced multimodal models for more complex tasks.
The defence was attended by over 30 people. We had three candidates that day, all in the context of EuroCC2 and EuroCC4SEE
Ms. Enisa Trubljanin successfully defended her master’s thesis titled “Deep Learning with Application in Education” at the Faculty of Information Systems and Technologies, University of Donja Gorica. The development and testing of these solutions were supported by high-performance computing (HPC) resources provided through the EuroCC initiative in Montenegro.
Ms. Enisa Trubljanin
ABSTRACT – This master’s thesis explores the potential application of deep learning in education through the development and evaluation of two concrete solutions: an intelligent chatbot for solving matrix problems and a model for detecting cheating during online exams by analyzing eye movement. The first part of the thesis provides a theoretical foundation of deep learning, with a focus on neural networks, their architectures, transfer learning, and evaluation metrics. The practical part presents the development of a chatbot based on advanced language and mathematical models, implemented using high-performance computing cluster resources, enabling students to engage in interactive mathematics learning. Additionally, a model for detecting cheating through gaze analysis was developed, trained on the Columbia Gaze Dataset, and integrated into an online exam proctoring system. Evaluation results demonstrate a high level of accuracy and user satisfaction for both solutions. Beyond the technical aspects, the thesis also addresses ethical issues and privacy concerns related to the use of artificial intelligence in educational settings. Based on the findings, the study highlights the broad range of potential applications of deep learning in modern educational systems.
ITAS is in the implementation phase of the project “AI system for pathological analysis of adenocarcinoma (PathAI)”, co-financed by the Innovation Fund of Montenegro, when processing the collected images requires GPU resources that exceed locally available resources, so the application to the EuroHPC JU Development call was recognized as the optimal solution. To this end, the Director of ITAS, Ivan Bošković, requested assistance from the NCC Montenegro team in preparing the application.
Prof. Enis Kočan, a member of the NCC Montenegro team, visited ITAS on Tuesday, June 24, and provided all the information and technical details necessary for preparing the application to the EuroHPC JU Development call. ITAS will apply for the necessary GPU resources to the Development call by the end of June.
prof. Enis Kocan (UCG) and mr Ivan Boscovic (ITAS)
In addition, during the meeting, other projects that ITAS is currently working on were discussed, as well as possibilities for cooperation with the NCC Montenegro team. Prof. Enis Kočan presented to Director Bošković the second open Fortissimo plus call for Business Experiments, intended for small and medium-sized enterprises.
