HPC Montenegro - National Competence Centre for High-Performance Computing

05.11.2026.05.11.2026.

HPC Montenegro at CIGRE NGN 2026: Advancing AI and HPC Applications in the Energy Sector

Representatives of the National Competence Centre for High-Performance Computing (HPC Montenegro) participated in the CIGRE Next Generation Network (NGN) 2026 workshop for students and young engineers, held on November 4, 2026, at the Science and Technology Park of Montenegro in Podgorica. The event attracted 74 registrations, including representatives of major energy companies such as EPCG, CEDIS, and CGES, other industry stakeholders, and academia.

Prof. Tomo Popović (University of Donja Gorica) delivered a lecture titled “Artificial Intelligence in Energy – Opportunities and Limitations”, prepared jointly with Dr. Luka Filipović (UDG) and Prof. Božo Krstajić (University of Montenegro). The presentation explored practical applications of AI and machine learning in electrical power systems, including energy demand and generation forecasting, predictive maintenance, grid management, and energy system optimization.

Special attention was given to the role of High-Performance Computing (HPC) in supporting advanced AI models and large-scale simulations. Participants were introduced to the EuroCC3 project, HPC Montenegro’s activities and services, and opportunities for expert consultations, training, and access to European supercomputing infrastructure. The presentation also highlighted selected HPC and AI success stories relevant to the energy sector from Montenegro and across Europe, demonstrating the benefits of collaboration with National Competence Centres.

Following the lecture, Prof. Popović moderated the panel discussion “Opportunities for AI Applications in Electrical Power Systems”, featuring Mihailo Micev (UCG), Krsto Biskupović (CEDIS), Ivan Vujović (TERING), and Stevan Šandi (BIXBIT/INNO4GRID).

Participation in the workshop provided a valuable opportunity for HPC Montenegro to engage directly with energy sector stakeholders and young engineers, exchange experiences, and establish new industry contacts. Such activities strengthen academia–industry collaboration, raise awareness of EuroCC3 opportunities, and help identify potential areas for future cooperation in applying HPC and AI technologies to the energy sector [link].

04.11.2026.05.11.2026.

Cyber Co awarded Fast Lane Access for the third time and Joins NVIDIA Inception Program for Startups

GetMoved, an AI platform for the moving industry developed by the Cyber Co Startup, has been awarded free access to GPU resources on the Leonardo supercomputer at CINECA in Italy — its third EuroHPC JU allocation through the Fast Lane access. The reason for changing from the LUMI to Leonardo supercomputer is in that Cyber CO has joined NVIDIA Inception, NVIDIA’s programme for technology startups and should now use NVIDIA graphical casrs, like the ones at Leonardo AI factory. Together the three awards have carried GetMoved’s computer vision work from the first experiments to production training runs.

GetMoved turns a short smartphone walkthrough of an apartment into a structured household inventory, a volume and weight estimate, and a moving cost estimate. The platform combines a customer marketplace, an ERP for moving companies, and the inventory engine in a single product. The detection models cover 78 object classes and are supported by a complete training pipeline — dataset preparation, annotation, training, evaluation, and continuous retraining.

Leonardo provides compute continuity as that pipeline moves toward production. NVIDIA Inception adds access to NVIDIA’s model catalogue and development tooling, along with compute credits; the company has opened an organisation on NVIDIA Build and NGC. On the technical roadmap, the current detector is being evaluated against a vision-language model, NVIDIA Cosmos, in phases with a decision gate before each replacement step.

The platform launched in the New York and New Jersey market in June 2026 and works with licensed moving companies on a non-exclusive basis.

27.10.2026.27.10.2026.

HPC Montenegro and HashiMotto Explore HPC and AI Opportunities in Healthcare

The National Competence Centre for High-Performance Computing (HPC Montenegro) held an initial consultation meeting at the University of Donja Gorica (UDG) with HashiMotto, an innovative startup from Podgorica, to explore opportunities for applying high-performance computing (HPC) and artificial intelligence (AI) in healthcare. The meeting was attended by Omar Radončić, Director of HashiMotto, and HPC Montenegro representatives Tomo Popović and Luka Filipović.

Representatives of HashiMotto and HPC Montenegro during initial consultations at UDG

HashiMotto is developing an AI-powered application for individuals with Hashimoto’s thyroiditis, offering personalized support, health monitoring, and medical data analysis. Discussions focused on the potential development and training of predictive AI/ML models using large-scale medical datasets, particularly for applications in endocrinology and personalized treatment monitoring.

HPC Montenegro presented opportunities for accessing local HPC computing nodes, technical assistance with configuring computational environments and running AI/ML experiments, and support for accessing European supercomputing resources through the EuroHPC Benchmark Access and Development Access calls.

This consultation reflects HPC Montenegro’s continued commitment to supporting local startups and innovative companies in developing advanced AI solutions and adopting HPC technologies.

25.10.2026.25.10.2026.

MSc Thesis: Computer Vision Models for Liver Tumor Segmentation

On October 21, 2026, MSc candidate Ida Mušović completed her thesis entitled “Development of Computer Vision-Based Predictive Models for Early Diagnosis of Liver Tumors” within the Artificial Intelligence Master’s programme at the Faculty for Information Systems and Technologies, University of Donja Gorica, under the mentorship of Prof. Dr. Tomo Popović.

The research and experimental work were partially supported by NCC Montenegro through HPC infrastructure and expertise within the EuroCC3 project. Through its support for academic research and the development of AI and HPC competencies, NCC Montenegro contributes to advancing the application of advanced computing technologies in healthcare.

Examples of CT images and anatomical segmentation masks generated using the TransUNet model (Source: Ida Mušović, MSc thesis, 2026).

The thesis investigates the application of deep learning and computer vision to automated liver tumor segmentation in CT images, focusing on the comparison of convolutional, hybrid, and transformer-based architectures for computer-aided medical image analysis.

ABSTRACT – This thesis presents the development and comparative evaluation of three deep learning architectures—U-Net, TransUNet, and SegFormer—for automated liver tumor segmentation in CT images. Using the publicly available Liver Tumor Segmentation (LiTS) dataset and a 2.5D image-processing approach, the models were evaluated using Dice similarity and Intersection over Union metrics, with additional analysis of performance across different lesion sizes. SegFormer achieved the highest overall Dice score (82.86%) for tumor segmentation, although differences between the architectures were not statistically significant at the patient level. The study also identified lesion size as an important factor influencing segmentation accuracy, with smaller tumors presenting greater challenges. The findings demonstrate the potential of computer vision to support medical image analysis while emphasizing the need for further validation before clinical application.

24.10.2026.

MSc Thesis Defence: Generating and Evaluating Exam Questions Using LLMs and HPC

On October 21, 2026, MSc candidate Sara Kovačević successfully defended her thesis entitled “Generation and Quality Evaluation of Exam Questions Using Large Language Models and HPC Infrastructure” within the Artificial Intelligence Master’s programme at the Faculty of Information Systems and Technologies, University of Donja Gorica (UDG). The thesis was supervised by Prof. Dr. Tomo Popović. The research was partially supported by NCC Montenegro within the EuroCC3 project, including access to HPC infrastructure and support for experimental work. The study demonstrates the potential of combining generative AI and high-performance computing to support the preparation and evaluation of educational materials, with particular attention to the Montenegrin language.

Ms Sara Kovacevic at her MSc thesis defence

ABSTRACT – This thesis presents the development and evaluation of an automated system for generating exam questions from video lectures using large language models (LLMs). The system integrates automatic transcription, extraction of key concepts, generation of multiple-choice and short-answer questions, and automated quality assessment. Nine LLM configurations were evaluated in English and Montenegrin, using local computing resources, the UDG HPC cluster, and API-based inference services.

The evaluation combined linguistic and structural metrics, Bloom’s taxonomy, and an LLM-as-Judge approach to assess the quality of generated questions. Experimental results highlighted challenges faced by smaller models when generating educational content in Montenegrin, while larger models achieved more consistent results. Experiments on the UDG HPC cluster demonstrated a 17.9× GPU speedup compared with CPU inference for the same model.

The research highlights the potential of AI and HPC technologies to assist educators in preparing assessment materials, while emphasizing the importance of human review and further pedagogical validation before practical deployment.

24.10.2026.24.10.2026.

MSc Thesis Defence: Explainable AI for Type 2 Diabetes Risk Assessment

On October 21, 2026, MSc candidate Sanja Lazić successfully defended her thesis entitled “Interpretable Machine Learning and Deep Learning Models for Type 2 Diabetes Risk Prediction Based on Behavioural and Clinical Indicators” within the Artificial Intelligence Master’s programme at the University of Donja Gorica, Faculty for Information Systems and Technologies, under the mentorship of Prof. Dr. Tomo Popović.

The research and experimental work were partially supported by NCC Montenegro through HPC infrastructure and expertise within the EuroCC3 project, with additional research support from the AI-AGE project. Through its involvement in academic research, mentoring, and competence development, NCC Montenegro contributes to strengthening the application of advanced AI and HPC technologies in healthcare and other scientific domains.

Ms Sanja Lazić during her MSc thesis defence

The thesis investigates the application of machine learning, deep learning, and explainable AI techniques to type 2 diabetes risk assessment, focusing on predictive performance, reliability, and interpretability. The research also demonstrates how AI models can support preventive healthcare through accessible, questionnaire-based risk assessment without requiring laboratory measurements.

ABSTRACT – This thesis presents the development and systematic evaluation of machine learning and deep learning models for type 2 diabetes risk assessment using behavioural, clinical, and demographic indicators. The study analysed 253,680 records and compared five models: Logistic Regression, Random Forest, XGBoost, LightGBM, and a deep neural network. Model performance was evaluated in terms of discrimination, probability calibration, and interpretability. While the deep neural network achieved the highest ROC-AUC (0.7728), LightGBM demonstrated the most balanced overall performance. Explainable AI methods, including TreeSHAP, Integrated Gradients, and DeepLIFT SHAP, were applied to identify factors contributing to model predictions. A prototype web application was developed to demonstrate how estimated risks and their explanations could be presented to users.

The results demonstrate the potential of explainable AI for supporting non-invasive risk assessment and preventive screening, while highlighting the importance of model reliability, transparency, and further validation before clinical application.

22.10.2026.24.10.2026.

UDG and FiveG Explore Further Collaboration in AR/VR, Generative AI and HPC

The University of Donja Gorica (UDG) hosted representatives of FiveG (Five Group) to discuss new opportunities for collaboration in augmented and virtual reality (AR/VR), generative artificial intelligence (AI), and high-performance computing (HPC).

FiveG has already successfully accessed EuroHPC resources with the support of NCC Montenegro through both Benchmark and Development calls for its Wasco AI project, utilizing the Leonardo Booster supercomputer. The meeting also addressed opportunities for further EuroHPC access applications to support the company’s ongoing AI development activities.

UDG hosted FiveG representatives to discuss further cooperation in AI, AR/VR and HPC

Building on an earlier initiative for cooperation between UDG and FiveG on establishing an AR/VR and AI laboratory, participants discussed the possibility of developing a demonstration setup for academia and industry. The proposed activities would showcase AR/VR applications and explore how generative AI and HPC resources could support the development of immersive digital content.

The meeting reaffirmed the shared interest in connecting academic research, education, and industry innovation through practical applications of emerging technologies.