ERCIM News 143
January 2026
Special theme AI for Science
Guest editors Edina Nemeth (SZTAKI) and Alexandre Termier (University of Rennes – Inria/IRISA, France)
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by the guest editors Edina Nemeth (SZTAKI) and Alexandre Termier (University of Rennes – Inria/IRISA, France)
Advancing Discovery, Enhancing Trustworthiness, and Reshaping Scientific Practices
Artificial Intelligence (AI) is rapidly transforming the way science is conducted. From accelerating the discovery of new materials to modeling complex climate systems and supporting biomedical research, AI has become an essential tool for advancing knowledge. By enabling more efficient data analysis, powerful simulations, and new forms of hypothesis generation, AI helps researchers tackle problems that were previously too complex or time-consuming to solve.
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by René Berndt, Hillary Farmer and Eva Eggeling (Fraunhofer Austria)
Improving the reviewer selection process for conferences and journals using AI and large language models (LLMs) can significantly enhance both efficiency and quality. AI-driven systems can analyse manuscripts and match them with potential reviewers based on their expertise, publication history, and prior reviewing experience. By leveraging semantic understanding rather than relying solely on manually assigned keywords, LLMs enable more accurate and nuanced reviewer–paper alignment.
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by Rita Stampfl (University of Applied Sciences Burgenland), Barbara Geyer (University of Applied Sciences Burgenland)
At the University of Applied Sciences Burgenland, a GPT-based chatbot has been developed to support students in creating research topic proposals for scientific papers. Large Language Models like GPT-4 enable interactive conversations, allowing chatbots to facilitate complex learning processes and provide personalised learning experiences. In the rapidly changing educational landscape, specially designed educational chatbots are gaining importance. This trend, combined with the accessibility of Large Language Models and the ability to create GPTs without programming knowledge, opens new possibilities for integration them into learning environments. To ensure these chatbots function as intelligent tutors rather than simple question-answer machines, appropriate instruction is essential.
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by Susie Ruston McAleer (21c) and Spiros Borotis (Maggioli S.p.A)
THEMIS 5.0 is generating evidence, tools, and methods that help scientific communities evaluate when AI systems can be trusted. Through pilots in healthcare, maritime operations, and journalism, the project is exploring how trustworthiness assessments can help organizations take up AI in a responsible manner.
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by Christian Beecks (FernUniversität in Hagen) and Markus Lange-Hegermann (Technische Hochschule Ostwestfalen-Lippe)
Data-driven scientific discovery increasingly relies on artificial intelligence. This article presents a human-centred data science framework based on Gaussian process models which enable the extraction of interpretable, uncertainty-aware insights while keeping the data scientist in control of the discovery process.
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by Iordanis Sapidis, Michalis Mountantonakis and Yannis Tzitzikas (FORTH-ICS and University of Crete)
SemanticRAG [1] is an interactive QA system that answers questions using both documents and Knowledge Graphs. To mitigate the black-box nature of LLMs, it provides provenance for every answer, citing the exact document snippet or KG triple from which it originates so users can verify each claim.
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by George Hatzivasilis and Sotirios Ioannidis (Technical University of Crete) and François Hamon (Greencityzen)
Environmental sciences increasingly rely on AI foundation models to integrate heterogeneous data and support sustainable decision-making. Their real-world impact, however, depends on security, trustworthiness, and resilient deployment, as illustrated by secure smart watering systems.
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by Luca Ciampi, Ludovico Iannello, Giuseppe Amato (CNR-ISTI), Federico Cremisi and Fabrizio Tonelli (Scuola Normale Superiore Pisa)
Can living neurons compute? Researchers from CNR-ISTI, CNR-IBF, and Bio@SNS introduce a pioneering approach in which cultured neuronal networks act as reservoirs for pattern recognition. This bio-hybrid paradigm aims to bridge neuroscience and machine learning, opening new pathways towards interpretable and energy-efficient AI.
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by Enrico Barbierato and Matteo Montrucchio (Catholic University of the Sacred Heart)
Blockchains are often proposed as trustworthy backbones for AI-driven science, yet their energy costs remain poorly understood. As research infrastructures scale, these costs become a constraint rather than a footnote. This article asks what blockchain energy consumption really implies for sustainable scientific computing.
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by Enrico Barbierato and Alice Gatti (Catholic University of the Sacred Heart)
AI models are becoming ever larger and more energy-intensive, raising questions about how scientific knowledge is produced. This article argues that computational efficiency is essential for reproducible, transparent and sustainable AI-driven science.
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by Attila Bekkvik Szentirmai (University of South-Eastern Norway)
A browser-based augmented reality research platform demonstrates how lightweight, on-device AI can function as scientific infrastructure. By lowering technical and ethical barriers, it enables fast, privacy-preserving experimentation with computational sensing in real-world settings and across diverse user groups.
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by Tristan van Leeuwen, Felix Lucka and Ezgi Demircan-Tureyen (CWI)
An image says more than a 1000 thousand words, it is said. This also holds true in many scientific applications, where 2D, 3D, or even 4D images are analysed. But how do we compute images from raw measurements, and can AI help us improve? At CWI’s Computational Imaging group in Amsterdam, mathematicians and computer scientists are trying to answer these questions.
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by Tatjana Ćeranić, Stephan Schraml and Philip Taupe (AIT Austrian Institute of Technology GmbH)
Reliable detection of small, local changes in real airborne laser scanning data remains difficult with current 3D change-detection techniques. Off-the-shelf methods often overlook subtle modifications or flag too many false positives. By combining semantics with geometry-based and deep-learning methods, we aim to improve robustness in noisy, cluttered settings.
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by Jean-Baptiste Burnet and Olivier Parisot (LIST)
Cyanobacteria blooms pose growing risks to drinking water supplies and recreational waters, a challenge intensified by climate change and inadequately captured by current regulatory monitoring strategies. Our study demonstrates how low-cost, ground-based RGB cameras combined with machine learning enable near real-time detection of blooms. By deploying automated photo traps and YOLO-based detection models at a major freshwater reservoir in Luxembourg, we open new pathways for early warning systems and improved understanding of harmful cyanobacteria bloom dynamics.
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by Julia Pöschl, Philip Taupe, Jakob Hurst (AIT Austrian Institute of Technology GmbH)
Staying informed is crucial for decision-makers, particularly in time-critical domains such as disaster response and public safety. The Enhanced Language Interpreter (ELI) presented here supports decision-makers in making use of information that is available but cumbersome to process. It analyses heterogeneous inputs using large language models and knowledge graphs and converts them into a concise representation tailored to the target domain, thereby enhancing the operational picture of the situation.
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by Adrián Segura Ortiz, José García-Nieto and Ismael Navas Delgado (ITIS Software, University of Málaga)
The resulting consensus networks are more robust and interpretable, enabling deeper insights into complex disease mechanisms.
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by Jamal Toutouh (University of Málaga, Spain), Sergio Nesmachnow (Universidad de la República, Uruguay), Martín Draper (Universidad de la República, Uruguay), and Maximiliano Bove (Universidad de la República, Uruguay)
Data-driven and physics-informed generative adversarial networks provide fast surrogates for wind-turbine wakes, bridging high-fidelity simulation and wind farm design.
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by Eleftherios Christofi (The Cyprus Institute) Vagelis Harmandaris (The Cyprus Institute, University of Crete and FORTH-IACM)
By enhancing multi-scale molecular simulations with deep learning, AI is enabling researchers to bridge modelling scales that were once out of reach. This synergy opens new possibilities for understanding complex materials and accelerating scientific discovery.