Contribute your expertise to the Strategic Research & Innovation Agenda (SRIA) and advance AI-driven scientific discovery.
Want to nominate a colleague? Send an email to info[at]sciance[eu]
About AISWGs
The AI in Science Working Groups (AISWGs), established under the Horizon Europe-funded SCIANCE project, bring together experts in artificial intelligence, scientific research, research infrastructures, HPC, Open Science and innovation to co-create the European Strategic Research & Innovation Agenda (SRIA) for AI in Science.
Duration: April 2026 – June 2028. Members may withdraw; the consortium may replace members to maintain continuity.
Participation: working groups members are expected to participate in a limited number of online meetings (2 per year) and to assist in one in-person workshop. Members might receive additional requests for (asynchroneous) consultations and document reviews.
Benefits for WG Members
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Shape AI in Science: Influence research and innovation priorities.
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Collaborate Across Disciplines: Join interdisciplinary workshops and co-creation activities.
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Drive Consensus and Impact: Contribute to AISWG outputs and the SRIA.
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Leverage Your Insights: Share knowledge, data, and methods.
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Amplify Influence: Disseminate results within professional networks.
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Foster Transparency and Innovation: Support cross-domain collaboration.
AISWG Overview
SCIANCE will establish 10 Thematic Working Groups
Astronomy and Fundamental Physics
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Research in astronomy and fundamental physics, domain-specific AI applications and data science, HPC and AI infrastructures, research infrastructures (CERN, SKA, LOFAR)
Materials Science
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Research in materials science, domain-specific AI applications and data science, HPC and AI infrastructures, research infrastructures (CERIC-ERIC, ESRF, ILL, NOMAD)
Life Sciences
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Research in life science, ethical, legal and societal implications (ELSI) for life science data, domain-specific AI applications and data science, HPC and AI infrastructures, research infrastructures
Earth Sciences
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Research in climate science, agriculture, land use, water resources, ecosystems, biodiversity and urban studies, Earth observation systems, domain-specific AI applications and data science, HPC and AI infrastructures, research infrastructures
Social Sciences and Humanities
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Research in social sciences and humanities, domain specific AI applications and data science, HPC and AI infrastructures, research infrastructures (heritage, health, data archives, arts and humanities)
AI Research and Experimental Design
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Autonomous laboratories, high-throughput experimentation, physics-informed and hybrid AI models, LLMs and NLP for science, AI co-scientist, lab and workflow automation, interdisciplinary AI model development and data standards, AI skills development
Data Science and Advanced Analytics
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Data governance and lifecycle management, semantic interoperability, data quality and preparation for AI, scalable and distributed data engineering, advanced analytics, data visualisation, synthetic data generation, metadata and open research libraries
Open Science and Trustworthy AI
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Open and trustworthy AI for scientific research, integrity and reproducibility, open science policy, legal and IPR frameworks (AI Act, GDPR), data protection, privacy and ethic, data-sharing regulations
AI and Computing Infrastructures
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HPC, AI Factories, EOSC ecosystems, scholarly knowledge graphs, data labs integrated with AI infrastructures, research software, operational coordination of research and AI infrastructures, infrastructure policy and governance, ESFRI infrastructures
Industry, Innovation and Frugal AI
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AI innovations for scientific research, AI-enabled laboratory systems and workflows, AI Factories across pilot areas, cloud providers for research, GenAI for science, AI industry technology developments, frugal and resource-efficient AI
Who Should Apply?
We are looking for recognised experts, emerging leaders, infrastructure operators, AI innovators, and cross-disciplinary researchers who want to shape European AI in science priorities at policy and infrastructure level.
Estimated Time Commitment
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1 in-person workshop (1–2 days)
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2 online meetings per year (1.5–2h each)
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Occasional document review (asynchronous)
Eligibility
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Expertise in one or more relevant scientific domains or AI methods
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Experience with research infrastructures, data, or AI for science
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Open Science, citizen science, trustworthy/frugal AI, or AI industry
Selection Criteria
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Expertise: Relevant scientific or technical knowledge
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Contribution: Availability for workshops, online sessions, and SRIA review
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Diversity & Coverage: Balanced representation by discipline, country, career stage, gender, and sector
Application Process
1.Submit your application by 10 April 2026:
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Self-nomination: Complete the online application form*
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Third-party nomination: Email us with nominee details
2. Evaluation by the SCIANCE consortium panel
3. Notification and onboarding to WG collaboration platforms
*Personal data will be processed in accordance with the SCIANCE Privacy Policy.
Timeline
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Applications close: 10 April 2026
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Evaluation completed: Mid-May 2026
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First WG workshop: June 2026
What You Will Do as an AISWG Member
By joining an AISWG, you will actively contribute to shaping the future of AI in science. Expected activities include:
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Participate in Workshops
- Join at least one in-person co-creation workshop to discuss research priorities, infrastructure needs, and collaboration opportunities.
- Workshops involve experts from different AISWGs to facilitate interdisciplinary discussion and are co-located with relevant scientific conferences and infrastructure events where feasible.
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Engage in Online Collaboration
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Join online meetings at least twice per year to review outputs and build consensus.
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Engage asynchronously on our online community platform to comment, provide feedback, and refine WG contributions.
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Share your insights and expertise to enrich cross-domain discussions.
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SRIA Contribution
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Review and contribute to the Strategic Research & Innovation Agenda (SRIA).
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Help validate research priorities, infrastructure scenarios, and community recommendations.
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Knowledge Sharing & Outreach
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Disseminate working group results within your professional network, research communities, and relevant initiatives.
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Promote transparency and cross-fertilisation of AI expertise across disciplines.
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Maintain Confidentiality
- Treat all non-public materials, draft documents, and discussions as confidential.
