Europe’s position in the current cloud market needs to be improved. This market is currently dominated by non-European players by 75%, shaping the way that Europe is deploying and using cloud services. Although these players are bound to laws and regulations of foreign powers, such as PR China and USA, generating legitimate concerns for the EU, its businesses and citizens. EU’s digital future resides on having installed secure, high-quality data processing capacity. This can only be offered by cloud services both centrally and at the edge. In this context NOUS’s ambition is completely in line with the European Strategy for data as aims to create the foundations for a European Cloud Service which exploits the HPC network and tackles specific-to-the-EU-economy requirements as well as leverages different data spaces (Mobility, Energy, Green Deal and Manufacturing).

Machine learning algorithms often rely on large training datasets to achieve high performance. However, in domains like chemistry and materials science, acquiring such data is an expensive and laborious process, involving highly trained human experts and material costs. Therefore, it is crucial to develop strategies that minimize the size of training sets while preserving predictive accuracy. The objective is to select an optimal subset of data points from a larger pool of possible samples, one that is sufficiently informative to train an effective machine learning model.

Machine learning (ML) accelerates materials discovery by mapping structure-property relationships, but its efficiency is often limited by the need for massive datasets. To address this, we framed the search for high-performance nanoporous materials (MOFs/COFs) as a Bayesian Optimization (BO) problem.

This framework provides a data-efficient, computationally accessible route for navigating massive design spaces to find the most promising materials for adsorption and diffusion applications.

Integrating High-Performance Computing (HPC) with Quantum Computing (QC) marks a transformative shift in solving complex, intractable problems. This manuscript reviews the current landscape of HPC-QC interfacing, focusing on architectural strategies, software stacks, and hardware integration. It assesses various integration models, ranging from standalone systems to tightly-integrated, on-node hardware, while analyzing essential software frameworks such as Qiskit, PennyLane, and CUDA-Q.

As Quantum Computing (QC) evolves with increasing qubit counts, there is growing interest in applying its mechanics to solve large-scale, real-world problems that remain intractable for classical High-Performance Computing (HPC). Because QCs are not general-purpose machines, integrating Quantum Processing Units (QPUs) into HPC systems as specialized accelerators – much like CPUs and GPUs – offers a promising path toward substantial performance gains. By leveraging quantum properties like superposition and entanglement, these hybrid systems can address intensive computational tasks more efficiently.

Web 3.0 marks a transition toward a decentralized and transparent internet, utilizing blockchain and smart contracts to shift control from major corporations back to individual users. This evolution empowers people to own their data, enhance their privacy, and monetize their own content within a fairer digital ecosystem. Parallel to this, the Metaverse is emerging as an immersive virtual space where blockchain ensures the secure ownership and trade of digital assets like virtual land and art. Beyond social and economic opportunities, these technologies can support environmental sustainability by replacing physical goods with digital versions, thereby reducing waste.

This paper explores the convergence of cloud and high-performance computing (HPC), highlighting serverless computing as an efficient model for managing dynamic, compute-intensive workloads. By reviewing 122 articles from 2018 to 2025, the authors provide a new taxonomy and analysis of trends to guide the development of next-generation parallel solutions.

The NOUS Smart City Architecture (NSCA) is an extensible middleware designed to synchronize intelligent urban services across the edge–cloud continuum. By utilizing the lightweight MQTT protocol at the edge for low-latency communication among assets like vehicles and roadside units, and integrating the SIMPL-based Data Space Ecosystem in the cloud, NSCA ensures scalable data exchange and governance. These layers are seamlessly bridged by a flexible inter-broker connector that allows for efficient topic federation and minimal configuration for new service integration.