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ERCIM News

Publisher:
—
ISSN:
0926-4981
Category:
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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Latest articles

Reclaiming Software Engineering as a Key Enabler of the Digital Age - POSITION PAPER

2026-01-30

Peter Kunz

A joint Informatics Europe / ERCIM Working Group on Software Research has published the position paper “Reclaiming Software Engineering as the Enabling Technology for the Digital Age.” The paper argues that software engineering must be recognised as a foundational enabling technology underpinning advances across artificial intelligence, data-driven systems, cyber-physical systems, and digital infrastructures.

SAFECOMP 2026 45th International Conference on Computer Safety, Reliability and Security - CALL FOR WORKSHOP PROPOSALS

2026-01-30

Peter Kunz

Valencia, Spain, 22–25 September 2026 The 45th edition of the International Conference on Computer Safety, Reliability and Security (SafeComp 2026), to be held in Valencia in September 2026, will focus on the theme “Engineering safe and sustainable computing systems”, addressing the challenge of combining safety, security and sustainability in computing infrastructures. Founded in 1979, this conference brings together experts to share advances, experiences, and trends in the safety, reliability, and sustainability of critical systems.

A Human-Centred AI Approach to Data-Driven Scientific Discovery

2026-01-30

Peter Kunz

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.

A New ERCIM Working Group on Artificial Intelligence & Intelligent Systems (AIIS)

2026-01-30

Peter Kunz

by Giuseppe Manco (CNR) and the AIIS Working Group founding members Artificial Intelligence (AI) is rapidly reshaping the way we build software, run critical infrastructures, and deliver public services. From healthcare and mobility to cybersecurity and environmental monitoring, AI systems are becoming core components of Europe’s digital ecosystem. At the same time, the growing adoption of data-driven and foundation-model technologies is exposing major scientific and societal challenges: ensuring robustness, transparency, fairness, accountability, and alignment with European values and regulations.

Bridging Modelling Scales with AI: Deep Learning – Enhanced Multi-Scale Simulations of Molecular Systems

2026-01-30

Peter Kunz

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.

Accuracy Is Not Enough: Computational Efficiency and Scientific Knowledge in AI

2026-01-27

Peter Kunz

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.

SemanticRAG: Traceable Answers from Documents and Knowledge Graphs

2026-01-27

Peter Kunz

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.

What Promises do AI Hold for Computational Imaging?

2026-01-26

Peter Kunz

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.