The need to implement complex physics systems is critical across various scientific and engineering domains. However, traditional numerical models for simulating these systems are computationally expensive, requiring significant time, resources, and cost. Recent advancements in AI present a promising alternative, with AI models demonstrating the ability to capture the dynamics of complex physical systems. Despite these successes, AI models suffer from key limitations, including challenges with generalization, vulnerability to bias, ethical concerns, and accuracy, particularly when applied to unseen tasks or variable-range predictions. These limitations are collectively viewed as issues of robustness.

The TURING project aims to address these shortcomings by developing robust AI-driven solutions. It integrates multidisciplinary advancements from Machine Learning, Computer Engineering, Physics, and SSH to pre-train generative, multimodal foundation models capable of capturing the physics of dynamic systems that share common properties. Starting with a cautious approach, the models will incorporate representations of increasingly complex physical systems as robustness is ensured.

Once pre-trained, these foundation models will be fine-tuned for specific tasks, enhancing their domain-specific robustness. The tasks will target critical engineering and physics problems in nuclear energy, particle physics, and meteorology, which are of high priority for the EU. The task-specific and foundation models, collectively termed “TURING models”, will be developed in collaboration with partners from India, Canada, and Switzerland.

To maximize the impact of TURING models, the project will ensure compliance of its activities with regulations such as the EU AI Act and then publicly release those models, along with the TURING Framework (MLOps SW tools and web-based app with conversational capabilities), enabling developers and end users to leverage this technology for their applications.

Website: https://turing-project.eu
Linkedin: https://www.linkedin.com/company/turing-project/
X: https://x.com/Turing_project/
Zenodo community: https://zenodo.org/communities/turing-heu/

This project has received funding from the Horizon Europe Framework Programme (2021-2027) under the grant agreement No 101215032. 

The project develops five pilots that showcase AI across the energy landscape:
1️⃣ Smart Grids in Portugal: Advancing fault detection and resilience with AI-driven digital twins of the transmission system.
2️⃣ Microgrids in Spain: Predictive maintenance to keep renewable energy equipment reliable and efficient.
3️⃣ Hydrogen & Hybrid Energy Systems in France, Spain, and Italy: Optimizing electrolysers and hybrid plants through AI-powered digital twins.
4️⃣ Buildings in Latvia: Enhancing energy efficiency in residential complexes with AI-driven building digital twins.
5️⃣ Energy Communities in Italy: Improving the performance of local renewable energy communities through AI experimentation.

DSS Lab assumes the technical coordination of the Latvian pilot 4, where AI-powered digital twins are applied to multi-apartment residential buildings in Riga. By combining real consumption data with advanced modelling, the pilot explores how to optimize heating, cooling, and overall energy use in everyday living spaces. The aim is to demonstrate measurable efficiency gains and cost savings for residents, while contributing directly to Europe’s climate and digital transition goals. EFI-DSS tool developed by DSS Lab will play a key role in this process. More information on EFI-DSS can be found here.

Also, DSS Lab coordinates the Portuguese Pilot 1, where a digital twin of the country’s transmission system will be used along with forecasts of energy production and consumption. These forecasts will be provided by DeepTSF, which is an automated codeless forecasting application based on AI / ML algorithms, and developed by DSS Lab. The goal is to use these forecasts to validate energy market results while increasing efficiency and decreasing the imbalances of the grid. More information on DeepTSF can be found here.

The EnergyGuard project is co-funded by the Horizon Europe Programme of the European Union under grant agreement No. 101172705. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the granting authority can be held responsible for them.

EnergyGuard brings together five large-scale laboratories and Meluxina (Europe’s greenest high-performance computer) into a unified service. The result is a one-stop platform where startups, SMEs, and researchers can develop, test, and certify AI solutions in realistic conditions without real-world risks.

As coordinator, DSS Lab steers a pan-European consortium into a single, service-oriented facility. The network spans Portugal, Spain, France, Italy, and Latvia, with each site offering unique assets — from grid-scale digital twins and hydrogen production lines to microgrids and Soviet-era apartment blocks. A curated catalogue of digital twins, datasets, models, and APIs makes these resources accessible through a common cloud portal.

Complementing the physical labs is the Meluxina supercomputer in Luxembourg, powered entirely by renewable energy. Developers can scale simulations across thousands of CPU and GPU cores while maintaining a transparent carbon footprint. Preconfigured container workspaces with open-source libraries give even small teams access to petascale computing, with results delivered in minutes.

For innovators, the benefits are clear: dramatically shorter development cycles, lower capital expenditure and a structured path to compliance with the forthcoming EU AI Act. The TEF’s Acceptance Environment measures algorithms against a comprehensive risk database, covering cybersecurity threats, data bias, functional safety and more. Passing solutions receive a digital badge that signals trustworthiness to investors, customers and regulators alike.

Looking ahead, DSS Lab will use its role as coordinator to seed pilot projects, open new datasets, and mentor early-stage companies. By combining world-class infrastructure with transparent governance, EnergyGuard aims to position Europe as the go-to market for responsible, high-impact energy AI.

👉 Call to innovators: AI startups, researchers, and technology providers can request access or subscribe to updates at EnergyGuard TEF Project.

The EnergyGuard project is co-funded by the Horizon Europe Programme of the European Union under grant agreement No. 101172705. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the granting authority can be held responsible for them.

The project will integrate five significant European large-scale testing and experimentation facilities that cover the full energy value chain, supported by European’s greenest HPC infrastructure (Meluxina). This includes a digital twin (DT) of the Portuguese Transmission Network (RDN), the CEDER-CIEMAT Microgrid with its Distributed Energy Resources (DERs), the Hydrogen testing platforms at CEA LITEN, CARTIF, BER and CIEMAT, a high-fidelity local DT of Riga’s multi-apartment residential buildings and the Antrodoco Renewable Energy Community. The process requires a wide range of elements to cover diverse AI test needs, including wind power, photovoltaic systems, hydropower plant, AEM, PEM and SO eletrolyzers, fuel cells, EV charging stations, electric and public buses and battery storage systems. The facilities will be accessible to EnergyGuard end-users through a set of properly configured Digital Twins (DTs) and curated assets, including data, models, inference APIs, services, and applications through an AI development Testing environment.

EnergyGuard will enable easy seamless access to assets from the EU ecosystem including AIOD, Data Spaces, DIHs and other TEFs. Moreover, the project facilitates users to validate their products with an Acceptance Environment and a common open AI risks database for a wide range of cybersecurity and trustworthy AI assessments.

The TEF will serve as full infrastructure to support national AI regulatory sandbox initiatives and deliver 5 pilot cases for the private and public sector. EnergyGuard will build upon a long-term, self-sustainable business model driven by a new entity, incorporating market-ready features early in the design, such as a subscription/plan framework, billing, and professional support.

The EnergyGuard project is co-funded by the Horizon Europe Programme of the European Union under grant agreement No. 101172705.

The main goal of RE-EMPOWERED was to develop and demonstrate solutions for the energy transition of local energy systems based on multi-energy microgrids, interconnecting multiple energy vectors.

To fulfil its objectives, RE-EMPOWERED project has developed novel tools (energy management systems, smart meter, digital tools for community engagement and tool’s interfacing, energy planning, power electronic converters, charging station, resilient structures, air quality monitoring) for complete energy solutions for islanded /isolated communities. The ecoToolset consists of 10 tools (ecoEMS, ecoMicrogrid, ecoDR, ecoPlatform, ecoConverter, ecoVehicle, ecoMonitor, ecoResilience, ecoCommunity, ecoPlanning) that were developed and lab validated using advanced methods such as Hardware in the Loop and Digital Twin.  Next, the tools were successfully demonstrated in four demo sites complementary in terms of size, primary resources and technical maturity; two in Europe (Bornholm in Denmark and Kythnos/Gaidouromantra in Greece), and two in India (Ghoramara and Keonjhar).

The electrification of more than 700 households, the significant CO2 emission reductions at the installations, the commercialization of ecoMicrogrid tool and the formation of a citizen energy community in India prove the significant contribution of the project to the energy transition, the improvement of living conditions and the citizen engagement.

RE-EMPOWERED project has significantly enhanced citizens engagement through 41 training activities. Moreover, 13 scientific journal papers and 16 scientific conference papers were published, while 1 patent was also filed. Overall, the project’s results have been disseminated in more than 80 events.

More information can be found at RE-EMPOWERED website https://reempowered-h2020.com/

The THETIDA project will achieve this goal through the development of a preventive conservation strategy that includes monitoring, risk preparedness and management, for underwater and coastal (U&C-CH) sites, identify and ward off additional threats and promote adaptation, reconstruction and other post-disruption strategies to restore normal conditions to the historic areas, as well as long-term strategic approaches to adapt to climate change and to wield policy tools for economic resilience.

In this project, an interdisciplinary team of researchers, experts and practitioners will develop, test and validate an integrated multiple heritage risk assessment and protection system with evidence-based monitoring frameworks, innovative tools and instruments and through participatory processes (Citizens’ Science and Living Labs). The project implementation actions will link the social innovations with cutting-edge technologies (ICT and IoT harmonised tools).

The THETIDA project has received funding from the European Union’s Horizon Europe programme under grant agreement 101095253. 

Ergon-XR is an innovative training solution designed to enhance worker safety and efficiency in automated manufacturing. By leveraging extended reality (XR) technology, Ergon-XR provides a virtual environment where trainees can safely interact with industrial equipment—such as conveyors and collaborative robots—while minimizing ergonomic risks.

Trainees use XR headsets to practice tasks and learn best practices for reducing physical and cognitive stress. Meanwhile, trainers, equipped with their own headsets, monitor posture, repetitive movements, and cognitive load. The platform offers near real-time feedback based on standardized ergonomics metrics, enabling trainers to make dynamic adjustments and optimize training outcomes.

With customizable tools, Ergon-XR allows the creation of realistic, tailored workstations that align with individual trainee needs. By combining physical and cognitive risk assessments, Ergon-XR empowers workers to adapt to emerging technologies, fostering safer, healthier, and more productive manufacturing environments.

The Ergon-XR project is one of the winning projects of the MASTER-XR Open Call and it is led by the I-SENSE Group and CIBOS.

AutoMoTIF will focus on the development of strategies, business and governance models, regulatory recommendations and synergies that will enable the integration and interoperability of automated transport systems and solutions towards the operational automation of multimodal cargo flows and logistics supply chains in the intra-European network. It will list the gaps – both regulatory and technological – that are currently identified in automated transport technologies and logistics operations between modes and hubs through the analysis of automation demand and supply in multimodal transportation (users vs market). AutoMoTIF will follow an inclusive model to ensure that user and community needs are properly addressed and innovations are aligned with their expectations. UCs and scenarios will focus on real challenges and gaps in seamless automated logistics that will be simulated in real settings and different geographical locations.

The results of the UCs will be used to set up a master scenario addressing the end-to-end delivery of goods using highest degree of automation possible. The outcomes from current practices will be analysed, leading to the assessment of benefits that can be gained by exploiting automated transport means to seamless multimodal automatic cargo transport and vice versa. These benefits will be defined and analysed based on their social, environmental and economic impact, such as decreased emissions and congestion, improved working conditions and safety, as well as reduced logistics and freight transport costs, with the SSH aspects being a priority. AutoMoTIF will contribute to the enhancement of synergies among sectors and stakeholders, following European priorities, strategic partnerships, such as CCAM, Zero Emission Waterborne Transport and EU Rail JU to ensure that transferability of expected outcomes.

The project is funded by the European Union under the Horizon Europe call.

DELPHI is a revolutionary approach to address the growing challenges of passenger and freight mobility. Recognizing the complexity of stakeholder landscapes, fragmented transportation systems, and the need for secure data sharing DELPHI will integrate sectors, harmonize data, and leverage advanced methodologies, to transform transportation systems for a sustainable future.

DELPHI’s aim can be summarized into 5, Environmental, Economic, Societal and Scientific Objectives:

1. Make cities’ traffic flow better and reduce accidents, injuries, and traffic on roads.
2. Speed up decision-making and implementation by 30% while improving how decisions are made.
3. Develop 5 different business models that save money in data processing, transportation planning, and fuel usage.
4. Share DELPHI’s research findings and models with others for further study.
5. Contribute to at least 4 groups working on transportation and logistics improvements.

Over the course of the project’s three-year timeframe, there will be a total of four trials conducted across the participating transportation networks located in Spain, Greece, and Romania. These trials aim to facilitate effective preparation and seamless integration of systems, and will involve continuous monitoring of progress to ensure smooth functioning and interconnectivity.

Our group is the coordinated of the project as well as leader of activities in WP2 and tasks in WP3&4 related to multimodal, technologyagnostic services, interoperability and legacy systems, environmental, social, financial parameters for multimodal optimization.

DELPHI project has received funding under grant agreement No 101104263.

Raw materials serve as the backbone of the European Union’s digital and green economy, underpinning industrial operations and aligning with the objectives of Europe’s Green Deal. Recognizing the imperative to enhance resource accessibility, efficiency, and circularity, the EU emphasizes resilience in the raw materials sector as a cornerstone of sustainable development; and advocates for the utilization of Earth Observation (EO) technologies.

TERRAVISION’s EO Mining Services Platform will leverage four key innovation pillars to enhance the entire raw materials life cycle:

1. Data Ecosystem: Harnessing data from diverse sources to monitor the raw materials life cycle.
2. Analysis Ready Data and Raw Materials Spectral Library: Standardizing EO data for seamless integration with in-situ data, facilitating comprehensive spectral analysis
3. EO Services for the Mining Industry: Developing essential services critical to the supply chain (i.e., mapping of critical raw materials, mineral exploration, extraction rates in opencast mines and volume estimation) and enabling dynamic hazard mapping for proactive risk management
4. Green & Resilience Accountability: Quantifying the environmental and socio-economic impact of mining activities to promote sustainability and resilience in the supply chain

Through extensive validation campaigns across EU mining sites, TERRAVISION will demonstrate the efficacy of its platform, paving the way for widespread adoption of sustainable mining practices.

Our group is the coordinator of TERRAVISION leading WPs 9 &10 focused on developing and deploying EO services for the mining industry and technical tasks related with system specifications and architecture of TERRAVISION platform, seamless integration and connection with existing system, the development and deployment of RM exploration and extraction rates in open cast mining services. Additionally, as leader of dissemination and communication activities ICCS will effectively disseminate project’s results to maximize the exploitation and future use of the TERRAVISION EO service and platform.

TERRAVISION is an Horizon Europe project.