How to use this template
- Replace every
[Write your content here]block with your actual proposal text.- Replace every
[PLACEHOLDER]in tables with real data.-
[RAISE GUIDANCE: ...]notes are advisory — remove them before submission.- All tables are mandatory and must be retained.
- Page budget (informative): TOC ~1 p, Org list ~2 p, Section 1 ~15 p, Section 2 ~8 p, Section 3 ~7 p.
- The body text (from the start of Section 1 through the end of Section 3) must not exceed 30 pages.
| Section | Title | Page |
|---|---|---|
| List of Participating Organisations | Consortium member table; non-academic data table | 3 |
| 1 | Excellence | 5 |
| 1.1 | Quality and pertinence of the project's research and innovation objectives | 5 |
| 1.2 | Soundness of proposed methodology | 11 |
| 1.3 | Quality and credibility of training programme | 13 |
| 1.4 | Quality of supervision | 15 |
| 2 | Impact | 17 |
| 2.1 | Contribution to structuring doctoral training | 17 |
| 2.2 | Career perspectives and employability | 19 |
| 2.3 | Dissemination and exploitation plan | 20 |
| 2.4 | Magnitude and importance of contribution | 21 |
| 3 | Quality and Efficiency of Implementation | 23 |
| 3.1 | Work plan, risks, and effort | 23 |
| 3.2 | Quality of consortium | 31 |
Format note: This section counts toward the 2-page limit for organisational information (pages 3–4 of the document). It does NOT count toward the 30-page scientific body.
| Legal Entity Short Name | Academic ✓ | Non-Academic ✓ | Awards Doctoral Degrees ✓ | Country | Dept / Division / Laboratory | Scientist / Person-in-Charge | Role |
|---|---|---|---|---|---|---|---|
| [INSTITUTION-1] | ✓ | ✓ | [CC] | [Department / Lab name] | [Prof. First Last] | Coordinator (Beneficiary) | |
| [INSTITUTION-2] | ✓ | ✓ | [CC] | [Department / Lab name] | [Prof. First Last] | Beneficiary | |
| [INSTITUTION-3] | ✓ | ✓ | [CC] | [Department / Lab name] | [Prof. First Last] | Beneficiary | |
| [INSTITUTION-4] | ✓ | ✓ | [CC] | [Department / Lab name] | [Prof. First Last] | Beneficiary | |
| [INSTITUTION-5] | ✓ | ✓ | [CC] | [Department / Lab name] | [Prof. First Last] | Beneficiary | |
| [INSTITUTION-6] | ✓ | [CC] | [Department / Lab name] | [Prof. First Last] | Beneficiary | ||
| [COMPANY-1] | ✓ | [CC] | [Division name] | [Dr. First Last] | Beneficiary | ||
| [COMPANY-2] | ✓ | [CC] | [Division name] | [Dr. First Last] | Beneficiary | ||
| [INSTITUTE-1] | ✓ | [CC] | [Department / Lab name] | [Dr. First Last] | Associated Partner | ||
| [COMPANY-3] | ✓ | [CC] | [Division name] | [Dr. First Last] | Associated Partner | ||
| [INSTITUTION-7] | ✓ | ✓ | [CC] | [Department / Lab name] | [Prof. First Last] | Associated Partner linked to [INSTITUTION-2] |
[RAISE GUIDANCE: The consortium should cover a breadth of scientific domains where AI is transformative (e.g., structural biology, climate science, materials discovery, astrophysics, drug design). Include at least 2–3 non-academic beneficiaries (industry, national labs, research infrastructures) to ensure exposure to applied AI-in-science contexts. Ensure geographic spread across EU Member States and Associated Countries. A minimum of 3 beneficiaries from 3 different EU Member States is required; RAISE specifically encourages broad European participation to build the virtual institute vision.**
| Legal Entity Short Name | Country | Type of Organisation (SME / Large Enterprise / Public Body / Other) | Primary Activity / Sector | Annual Turnover (M€) or Budget (M€) | Number of Employees | Main Role in the Network |
|---|---|---|---|---|---|---|
| [COMPANY-1] | [CC] | [Large Enterprise / SME / ...] | [e.g., Pharmaceutical R&D / AI Software / ...] | [XX] | [XXXX] | [e.g., Industrial secondment host; co-supervision of DC-07, DC-11; provision of proprietary datasets for AI benchmarking] |
| [COMPANY-2] | [CC] | [Large Enterprise / SME / ...] | [e.g., Scientific Instrumentation / HPC services / ...] | [XX] | [XXXX] | [e.g., Industrial secondment host; co-supervision of DC-09; HPC resource provision for network-wide training] |
[RAISE GUIDANCE: Every non-academic beneficiary must demonstrate genuine scientific engagement — not merely administrative or hosting capacity. Describe the specific datasets, compute infrastructure, domain expertise, or translation pathways they bring to AI-in-science research.**
(Section 1 target: approximately 15 pages of the 30-page body)
[Write your content here]
[RAISE GUIDANCE: Open with a compelling framing of the scientific challenge: why does [your chosen scientific domain(s)] require fundamentally new AI capabilities — not incremental applications of existing tools? Position RAISE-[ProjectAcronym] within the broader RAISE (Research and Innovation for AI in Science in Europe) initiative. Articulate how this network contributes to RAISE's vision of a European virtual institute that pools resources, talent, and infrastructure for AI-driven scientific discovery. Reference the strategic relevance to the European Research Area and the competitiveness of European science globally. Recommended length: 0.5–1 page.**
Overall Objective
[Write your content here — one clear sentence stating the overarching scientific and training goal]
Specific Objectives
The specific objectives of [ProjectAcronym] are:
| # | Specific Objective | Addressed in WP(s) |
|---|---|---|
| SO1 | [Write your content here — e.g., Develop novel deep learning architectures for [scientific problem], surpassing the state of the art in [measurable metric]] | WP[X] |
| SO2 | [Write your content here — e.g., Create open benchmark datasets and evaluation frameworks for AI in [domain]] | WP[X] |
| SO3 | [Write your content here — e.g., Train 15 doctoral candidates at the frontier of AI and [scientific domain]] | WP[X], WP[X] |
| SO4 | [Write your content here — e.g., Validate AI-developed tools in real-world scientific workflows at [type of facility/partner]] | WP[X] |
| SO5 | [Write your content here — e.g., Build a sustainable European network for AI in [domain] research and training] | WP[X] |
[RAISE GUIDANCE: Objectives must be SMART (Specific, Measurable, Achievable, Relevant, Time-bound). Each objective should be traceable to one or more DCs and to deliverables/milestones. Avoid vague language such as "improve" or "explore" without quantifiable targets.**
State of the Art and Gaps Addressed
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[RAISE GUIDANCE: Provide a rigorous critical review of the state of the art in (a) the scientific domain(s) and (b) the AI/ML methodologies. Identify the specific gaps that current AI tools cannot address and explain WHY closing these gaps requires the development of new AI systems — not re-application of existing ones. This is the core intellectual justification for the RAISE eligibility criterion: AI must be integral and innovative, not merely instrumental.**
Beyond the State of the Art
[Write your content here]
[RAISE GUIDANCE: Explicitly describe what AI innovations each DC project will produce. Use language such as: "DC-03 will develop a novel physics-informed neural network architecture that..." rather than "DC-03 will use machine learning to analyse...". Reviewers will scrutinise whether AI development is genuinely new.**
[RAISE GUIDANCE: Each DC project must demonstrate that AI development is INDISPENSABLE to the scientific research goal — the DC must be developing innovative AI systems, models, algorithms, or tools for the scientific domain, not merely applying existing AI methods as a secondary technique. The scientific and AI components must be inseparable. Ensure secondments are scientifically justified and that the non-academic secondment (if applicable) provides genuine AI-in-science exposure.**
DC Project Summary Table
| DC # | Host Institution | Project Title | Primary Scientific Domain | AI Innovation Element | Expected Results | Secondment Partners | Duration (months) |
|---|---|---|---|---|---|---|---|
| DC-01 | [INSTITUTION-X] | [Write your content here] | [e.g., Structural Biology] | [e.g., Equivariant graph neural networks for protein-ligand docking] | [Write your content here] | [INSTITUTION-Y] (M13–18); [COMPANY-Z] (M19–24) | 36 |
| DC-02 | [INSTITUTION-X] | [Write your content here] | [e.g., Climate Science] | [e.g., Physics-informed transformer models for sub-grid parameterisation] | [Write your content here] | [INSTITUTION-Y] (M7–12); [INSTITUTION-Z] (M25–30) | 36 |
| DC-03 | [INSTITUTION-X] | [Write your content here] | [e.g., Materials Discovery] | [e.g., Generative diffusion models for inverse materials design] | [Write your content here] | [COMPANY-Y] (M13–18) | 36 |
| DC-04 | [INSTITUTION-X] | [Write your content here] | [e.g., Astrophysics] | [e.g., Simulation-based inference for gravitational wave parameter estimation] | [Write your content here] | [INSTITUTION-Y] (M19–24); [INSTITUTE-Z] (M7–12) | 36 |
| DC-05 | [INSTITUTION-X] | [Write your content here] | [e.g., Drug Discovery] | [e.g., Foundation model fine-tuning for ADMET property prediction] | [Write your content here] | [COMPANY-Y] (M13–24) | 36 |
| DC-06 | [INSTITUTION-X] | [Write your content here] | [e.g., Genomics] | [e.g., Attention-based multi-omics integration for regulatory element prediction] | [Write your content here] | [INSTITUTION-Y] (M7–12); [COMPANY-Z] (M25–30) | 36 |
| DC-07 | [INSTITUTION-X] | [Write your content here] | [e.g., High-Energy Physics] | [e.g., Fast surrogate models for Monte Carlo event generation] | [Write your content here] | [INSTITUTE-Y] (M13–18); [COMPANY-Z] (M19–24) | 36 |
| DC-08 | [INSTITUTION-X] | [Write your content here] | [e.g., Neuroscience] | [e.g., Latent variable models for large-scale neural population dynamics] | [Write your content here] | [INSTITUTION-Y] (M7–18) | 36 |
| DC-09 | [INSTITUTION-X] | [Write your content here] | [e.g., Earth Observation] | [e.g., Self-supervised learning for multi-modal satellite data fusion] | [Write your content here] | [COMPANY-Y] (M13–24); [INSTITUTION-Z] (M7–12) | 36 |
| DC-10 | [INSTITUTION-X] | [Write your content here] | [e.g., Chemistry] | [e.g., Reinforcement learning for autonomous reaction optimisation] | [Write your content here] | [COMPANY-Y] (M19–24); [INSTITUTION-Z] (M13–18) | 36 |
| DC-11 | [INSTITUTION-X] | [Write your content here] | [e.g., Medical Imaging] | [e.g., Uncertainty-aware convolutional architectures for pathology segmentation] | [Write your content here] | [COMPANY-Y] (M13–18); [INSTITUTION-Z] (M25–30) | 36 |
| DC-12 | [INSTITUTION-X] | [Write your content here] | [e.g., Ecology] | [e.g., Neural ODEs for ecosystem dynamics modelling from sparse sensor data] | [Write your content here] | [INSTITUTION-Y] (M7–12); [COMPANY-Z] (M19–24) | 36 |
| DC-13 | [INSTITUTION-X] | [Write your content here] | [e.g., Fluid Dynamics] | [e.g., Neural operators for turbulence closure in high-Reynolds-number flows] | [Write your content here] | [INSTITUTION-Y] (M13–18); [INSTITUTE-Z] (M25–30) | 36 |
| DC-14 | [INSTITUTION-X] | [Write your content here] | [e.g., Bioinformatics] | [e.g., Geometric deep learning for RNA tertiary structure prediction] | [Write your content here] | [INSTITUTION-Y] (M7–12); [COMPANY-Z] (M13–18) | 36 |
| DC-15 | [INSTITUTION-X] | [Write your content here] | [e.g., Quantum Science] | [e.g., Machine learning quantum error mitigation for near-term quantum devices] | [Write your content here] | [INSTITUTION-Y] (M19–24); [COMPANY-Z] (M25–30) | 36 |
Note on secondments: Each DC should have at least one planned secondment. Secondments at non-academic partners must be included for DCs where knowledge transfer to industry/society is scientifically relevant. Total secondment duration per DC should not exceed 30% of the DC's contract duration (i.e., max ~11 months for a 36-month DC).
Detailed Individual DC Descriptions
[RAISE GUIDANCE: For each DC, write a self-contained ~0.5-page description covering: (1) scientific background and gap, (2) AI innovation — what new AI system/method will be developed and why it does not exist yet, (3) research objectives and methodology, (4) expected results and their scientific/technological significance, (5) secondment plan and its scientific rationale. Total for all 15 DC descriptions: approximately 6–7 pages.**
DC-01: [Title]
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DC-02: [Title]
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DC-03: [Title]
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DC-04: [Title]
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DC-05: [Title]
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DC-06: [Title]
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DC-07: [Title]
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DC-08: [Title]
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DC-09: [Title]
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DC-10: [Title]
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DC-11: [Title]
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DC-12: [Title]
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DC-13: [Title]
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DC-14: [Title]
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DC-15: [Title]
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[RAISE GUIDANCE: Explain the collective innovation beyond what any single DC project achieves alone. Describe how the portfolio of 15 DCs creates synergies: shared datasets, shared AI model components, cross-domain benchmarking, and joint tools that benefit the entire RAISE initiative. Reference alignment with European AI strategy, the European Research Area priorities, and EU missions where relevant.**
[Write your content here]
[RAISE GUIDANCE: Describe the network's unified methodological framework for AI-in-science research. This should address: (a) how AI development methodology (e.g., ML engineering pipeline, model validation, reproducibility standards) is integrated with scientific domain methodology; (b) how the network ensures scientific rigour in AI claims (avoiding over-optimistic benchmark results, reporting negative results, using proper baselines); (c) how the network handles data — collection, curation, annotation, quality control, and sharing. Cite relevant methodological standards (e.g., NeurIPS reproducibility checklist, domain-specific reporting guidelines).**
[Write your content here]
[RAISE GUIDANCE: Demonstrate genuine interdisciplinarity: each DC sits at the intersection of at least one scientific domain and AI/ML. Describe how the training programme and joint research activities will prevent DCs from becoming isolated specialists in only one domain. Explain the specific mechanisms — joint seminars, cross-DC collaboration tasks, shared reading groups — that build integrative competence.**
[Write your content here]
[RAISE GUIDANCE: MSCA requires reflection on how gender/sex and other diversity dimensions are incorporated into the research content (not just HR practices). For AI in science: address algorithmic bias in AI models trained on historically non-representative scientific datasets; data diversity and representativeness; fairness in AI-assisted scientific discovery (e.g., AI models trained predominantly on data from high-income countries). Also include the network's own diversity targets for DC recruitment (minimum 40% of underrepresented gender). Mention planned unconscious bias training for supervisors.**
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[RAISE GUIDANCE: RAISE networks are expected to set a high standard for open science. Specify: (1) Open access publication commitment (Plan S / diamond open access); (2) FAIR data management — link to the Data Management Plan in Part B2; (3) Open-source release policy for AI models, training code, and benchmark datasets developed by DCs; (4) Pre-registration of computational experiments where applicable; (5) Use of open science platforms (e.g., Zenodo, HuggingFace, OpenML, Papers with Code). The release of open AI tools for the scientific community is a key RAISE deliverable.**
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[RAISE GUIDANCE: The training programme must be structured at doctoral level and must include DEDICATED training on AI in science — not generic programming or data science. This means: advanced ML theory, AI model development methodology, evaluation and benchmarking of AI systems in scientific contexts, responsible AI in science, and AI reproducibility. Distinguish between (a) Network-Wide Training Events (NWTEs, attended by all DCs), (b) local/institutional training at the host, and (c) transferable skills training. The total training load should be realistic — typically 15–30 ECTS over the doctoral period. Map training to career development stages.**
Training Programme Structure
| Training Module | Type | Target DCs | ECTS | Delivery Mode | Month(s) |
|---|---|---|---|---|---|
| AI Foundations for Science (methods, architectures, theory) | NWTE | All | 3 | Residential school | M4–5 |
| Scientific Computing and HPC for AI | NWTE | All | 2 | Residential school | M8 |
| Research Methods and Scientific Writing | NWTE | All | 2 | Workshop | M6 |
| Responsible AI in Science (bias, fairness, ethics, reproducibility) | NWTE | All | 2 | Workshop | M12 |
| Advanced AI Seminar Series (invited speakers, DC presentations) | NWTE | All | 2 | Virtual + residential | M1–36 |
| Industry Immersion Workshop | NWTE | All | 2 | Residential at industry partner | M18 |
| Entrepreneurship and Innovation | NWTE | All | 1 | Workshop | M24 |
| Science Communication and Policy | NWTE | All | 1 | Workshop | M30 |
| Domain-specific doctoral training (local) | Local | By host | 3 | Institutional courses | M1–12 |
| Transferable skills (local) | Local | By host | 2 | Institutional courses | M1–36 |
| Total | 20 |
Table 1: Main Network-Wide Training Events
| Event # | Title | Type | ECTS | Lead Institution | Participating Institutions | Action Month | Location | Duration (days) |
|---|---|---|---|---|---|---|---|---|
| NWTE-1 | Kick-off Meeting and Network Induction | Kick-off + induction | 0.5 | [INSTITUTION-1] | All | M1 | [City, Country] | 3 |
| NWTE-2 | Summer School I: AI Foundations for Scientific Discovery | Doctoral school | 3 | [INSTITUTION-2] | All | M5 | [City, Country] | 5 |
| NWTE-3 | Workshop: Scientific Computing and HPC for AI | Technical workshop | 2 | [INSTITUTION-3] | All | M8 | [City, Country] | 3 |
| NWTE-4 | Workshop: Research Methods, Open Science, and Scientific Writing | Skills workshop | 2 | [INSTITUTION-4] | All | M10 | [City, Country] | 3 |
| NWTE-5 | Annual Network Meeting I + DC Progress Symposium | Scientific meeting | 1 | [INSTITUTION-5] | All | M12 | [City, Country] | 2 |
| NWTE-6 | Workshop: Responsible AI in Science — Ethics, Bias, and Reproducibility | Skills + methods | 2 | [INSTITUTION-1] | All | M14 | [City, Country] | 2 |
| NWTE-7 | Summer School II: Advanced Topics in AI for Science | Advanced doctoral school | 3 | [INSTITUTION-3] | All | M18 | [City, Country] | 5 |
| NWTE-8 | Industry Immersion Week at [COMPANY-1 / COMPANY-2] | Industry engagement | 2 | [COMPANY-1] | All | M20 | [City, Country] | 5 |
| NWTE-9 | Annual Network Meeting II + Midterm Scientific Symposium | Scientific meeting | 1 | [INSTITUTION-6] | All | M24 | [City, Country] | 2 |
| NWTE-10 | Workshop: Entrepreneurship, Technology Transfer, and Spin-out | Transferable skills | 1 | [COMPANY-2] | All | M26 | [City, Country] | 2 |
| NWTE-11 | Science Communication, Outreach, and Policy Engagement | Transferable skills | 1 | [INSTITUTION-2] | All | M30 | [City, Country] | 2 |
| NWTE-12 | Final Network Conference: AI in Science — Results and Outlook | Dissemination conference | 1 | [INSTITUTION-1] | All + invited guests | M35 | [City, Country] | 3 |
[RAISE GUIDANCE: NWTE-2 and NWTE-7 (the summer schools) are the flagship training events and should be described in detail in the body text — covering content, invited instructors, and how they build AI-in-science competence. The Industry Immersion Week (NWTE-8) should be described with specific activities at the company partner (e.g., working on real industrial AI-in-science problems). NWTE-6 on Responsible AI is specifically encouraged by RAISE to ensure DCs are equipped to address societal and ethical dimensions of their work.**
Individual Development Plans (IDPs)
[Write your content here]
[RAISE GUIDANCE: Describe the IDP process: initial skills assessment at M1, annual review, supervisor and DC co-ownership of the IDP. The IDP should explicitly track AI-in-science competence development alongside domain expertise. Mention the network's commitment to the European Charter for Researchers and Code of Conduct for the Recruitment of Researchers.**
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[RAISE GUIDANCE: For each principal supervisor (and co-supervisor where relevant), briefly describe: (a) track record in AI research OR in the scientific domain (at least one supervisor per DC must have strong AI/ML expertise); (b) previous doctoral supervision record (number of completed PhDs); (c) experience with international, interdisciplinary projects. It is critical that each DC has at least one supervisor with genuine AI development expertise — not merely AI application experience. Provide a table of supervisors.**
Table: Supervisory Team Overview
| DC # | Principal Supervisor | Institution | AI Expertise | Domain Expertise | No. of Completed PhD Supervisions | Key Publications (AI or Domain, last 5 years) |
|---|---|---|---|---|---|---|
| DC-01 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Graph neural networks, geometric deep learning] | [e.g., Structural biology, protein folding] | [X] | [Write your content here] |
| DC-02 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Physics-informed ML, scientific machine learning] | [e.g., Climate modelling, NWP] | [X] | [Write your content here] |
| DC-03 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Generative models, diffusion models] | [e.g., Computational materials science] | [X] | [Write your content here] |
| DC-04 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Simulation-based inference, normalising flows] | [e.g., Gravitational wave astronomy] | [X] | [Write your content here] |
| DC-05 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Foundation models, transfer learning] | [e.g., Computational drug discovery] | [X] | [Write your content here] |
| DC-06 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Attention mechanisms, multi-modal learning] | [e.g., Genomics, regulatory genomics] | [X] | [Write your content here] |
| DC-07 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Surrogate modelling, emulation] | [e.g., High-energy physics, detector simulation] | [X] | [Write your content here] |
| DC-08 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Latent variable models, variational inference] | [e.g., Computational neuroscience] | [X] | [Write your content here] |
| DC-09 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Self-supervised learning, contrastive learning] | [e.g., Remote sensing, Earth observation] | [X] | [Write your content here] |
| DC-10 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Reinforcement learning, Bayesian optimisation] | [e.g., Synthetic chemistry, reaction design] | [X] | [Write your content here] |
| DC-11 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Uncertainty quantification, Bayesian deep learning] | [e.g., Medical image analysis, pathology] | [X] | [Write your content here] |
| DC-12 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Neural ODEs, continuous-time models] | [e.g., Ecology, biodiversity informatics] | [X] | [Write your content here] |
| DC-13 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Neural operators, operator learning] | [e.g., Computational fluid dynamics, turbulence] | [X] | [Write your content here] |
| DC-14 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Geometric deep learning, equivariant networks] | [e.g., RNA bioinformatics, structural bioinformatics] | [X] | [Write your content here] |
| DC-15 | [Prof. First Last] | [INSTITUTION-X] | [e.g., Quantum ML, variational quantum algorithms] | [e.g., Quantum information, quantum error correction] | [X] | [Write your content here] |
[Write your content here]
[RAISE GUIDANCE: Describe the formal supervision structure: thesis advisory committee composition (including at least one external member and, where possible, one non-academic member), meeting frequency, escalation pathway for supervisory conflicts, and the network's monitoring role via the Training and Supervision Committee. Explain how co-supervision across institutions is organised — joint supervision agreements, cost sharing, and IP arrangements. Align with the MSCA Charter and Code commitments.**
(Section 2 target: approximately 8 pages of the 30-page body)
[Write your content here]
[RAISE GUIDANCE: Describe how non-academic partners (industry, national labs, research infrastructures) contribute substantively to doctoral training — not merely as secondment hosts. This includes: co-designing training modules (especially NWTE-8 and domain-specific sessions), co-supervising DCs, providing access to proprietary datasets or computing facilities, contributing to the teaching programme of summer schools, and offering mentoring beyond the formal secondment period. For RAISE, the non-academic dimension should emphasise the AI translation pipeline: how industry partners help DCs understand the path from AI research prototype to deployed scientific tool.**
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[RAISE GUIDANCE: RAISE networks are expected to contribute to the long-term RAISE virtual institute infrastructure. Describe concrete sustainability plans: (a) continuation of the annual network conference as an open community event; (b) open-source maintenance plan for AI tools and datasets created by the network; (c) institutional commitments to continue PhD training in AI-in-science after the grant (e.g., new joint PhD programmes, chairs, centres); (d) plans to integrate network-developed training materials into permanent graduate curricula; (e) engagement with the RAISE data and compute infrastructure for continued use by the scientific community.**
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[RAISE GUIDANCE: The career landscape for PhD graduates with combined AI and domain science expertise is exceptionally strong. Describe the range of career pathways: (a) academic research (AI-focused or domain-science-focused, or bridging both); (b) industrial R&D in AI-for-science companies, pharma, tech; (c) national laboratories, research infrastructures, and large-scale scientific facilities; (d) policy, technology transfer, and start-up creation. Describe how the network's training, mentoring, and industrial exposure specifically prepare DCs for these pathways. Include plans for career tracking of DCs after the network ends (5-year follow-up survey). Mention the alumninetwork/community-building activities.**
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[RAISE GUIDANCE: Structure the plan around three distinct activities: (1) DISSEMINATION — targeting the scientific community: open-access publications, conference presentations, preprints, AI model releases on HuggingFace/Zenodo/GitHub; (2) EXPLOITATION — targeting economic/technological uptake: engagement with industrial partners beyond the consortium, technology licensing, spin-out creation; (3) COMMUNICATION — targeting the general public, policymakers, and students: press releases, social media, public lectures, science fairs, policy briefs. Define KPIs for each activity (e.g., number of open-access papers, GitHub stars on network repositories, public engagement events per year).**
Key Performance Indicators for Dissemination and Communication
| Activity | KPI | Target (over 4 years) |
|---|---|---|
| Open-access journal publications | Number of peer-reviewed papers | ≥ 45 (≥ 3 per DC) |
| High-impact venue publications (NeurIPS, ICML, Nature family, Science) | Number of papers | ≥ 10 |
| Open-source AI tools/models released | Number of GitHub repositories with active maintenance | ≥ 15 |
| Open datasets published on Zenodo/other FAIR repository | Number of datasets | ≥ 10 |
| Conference presentations (oral + poster) | Number | ≥ 60 |
| Network website unique visitors | Annual | ≥ 5,000 |
| Social media reach (LinkedIn, Twitter/X, Mastodon) | Followers | ≥ 2,000 |
| Public outreach events | Number | ≥ 20 |
| Policy briefs or contributions to consultations | Number | ≥ 3 |
| Media coverage (press, online news) | Number of items | ≥ 15 |
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[RAISE GUIDANCE: Address IP proactively. Key points: (a) By default, IP generated by DCs during secondments must be clearly allocated — specify in the Consortium Agreement and individual secondment agreements; (b) For AI models trained on proprietary industrial datasets, address ownership and licensing terms explicitly; (c) Default to open-source licensing (MIT/Apache 2.0) for AI tools developed using only public data and public funding; (d) When industrial partners co-develop tools, describe the licensing model that still enables academic publication; (e) Describe the role of the institutional technology transfer offices.**
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[RAISE GUIDANCE: Articulate the scientific impact at three levels: (1) WITHIN each scientific domain — what specific discoveries, capabilities, or paradigm shifts will the AI tools developed enable that were not possible before? Give concrete examples tied to specific DC projects. (2) FOR AI research — what novel AI methods, architectures, or theoretical insights will the network produce that advance the field of AI itself? (3) FOR THE RAISE INITIATIVE — how does this network contribute to building the RAISE virtual institute's scientific portfolio, shared infrastructure, and pan-European AI-in-science ecosystem? Emphasise that the network's outputs will be freely available to the entire European scientific community, amplifying impact beyond the consortium.**
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[RAISE GUIDANCE: Connect AI-in-science research to economic value. Describe: (a) How AI tools developed in the network will reduce costs or accelerate timelines in specific industrial sectors (drug discovery, materials development, climate services, etc.); (b) Spin-out and start-up potential — are any DC projects in areas with commercial applications? (c) Contribution to European technological sovereignty in AI for science — reducing dependence on non-European AI platforms for critical scientific applications; (d) HPC and AI infrastructure utilisation — does the network contribute to valorising European compute infrastructure investments (e.g., EuroHPC)?**
[Write your content here]
[RAISE GUIDANCE: The societal impact of AI-in-science research can be profound but must be articulated carefully and specifically. Address: (a) How AI-accelerated scientific discovery in the network's domains will contribute to societal challenges (health, climate, energy, food security — as applicable to your specific DC portfolio); (b) Equity and access dimensions: open-source tools make AI-driven science accessible to researchers in lower-resourced institutions; (c) Responsible AI practices embedded in the training programme ensure DCs will be ethical practitioners; (d) Public engagement with AI and science — countering AI hype and building scientifically literate public understanding of AI capabilities and limitations; (e) Contribution to training the next generation of responsible AI scientists for Europe.**
(Section 3 target: approximately 7 pages of the 30-page body)
[Write your content here — brief introduction to the work plan structure]
[RAISE GUIDANCE: The work plan should have a logical architecture: typically WP1 = Management and Coordination; WP2–WPn = Scientific Research WPs (one per domain cluster or AI methodology cluster); WPn+1 = Training and Supervision; WPn+2 = Dissemination, Exploitation, and Communication. Each DC must be unambiguously assigned to one primary scientific WP. Ensure the Gantt chart (if included as a figure) clearly shows DC start months, NWTE timing, deliverable and milestone due dates.**
| WP # | Title | Start Month | End Month | Lead Participant | Participating Institutions | DCs Involved | Objectives | Description |
|---|---|---|---|---|---|---|---|---|
| WP1 | Project Management and Coordination | 1 | 48 | [INSTITUTION-1] | All | All | O1: Ensure efficient day-to-day management; O2: Ensure financial and ethical compliance; O3: Coordinate across WPs and between DCs | [Write your content here — describe governance structure, management bodies (Steering Committee, Training and Supervision Committee, Ethics Board), reporting and monitoring procedures, internal communication tools, and contingency planning] |
| WP2 | [AI Research Domain Cluster 1, e.g., AI for Molecular Science] | 1 | 48 | [INSTITUTION-2] | [INSTITUTION-X, INSTITUTION-Y, COMPANY-Z] | DC-01, DC-05, DC-06, DC-14 | O4: Develop AI models for [domain cluster]; O5: Produce open benchmarks for [domain cluster] | [Write your content here — describe the unifying scientific questions, shared methodological infrastructure (datasets, compute, code), and how the DCs in this cluster collaborate and complement each other] |
| WP3 | [AI Research Domain Cluster 2, e.g., AI for Earth and Environmental Science] | 1 | 48 | [INSTITUTION-3] | [INSTITUTION-X, INSTITUTION-Y, COMPANY-Z] | DC-02, DC-09, DC-12, DC-13 | O6: [Write your content here]; O7: [Write your content here] | [Write your content here] |
| WP4 | [AI Research Domain Cluster 3, e.g., AI for Physics and Astronomy] | 1 | 48 | [INSTITUTION-4] | [INSTITUTION-X, INSTITUTION-Y, INSTITUTE-Z] | DC-04, DC-07, DC-15 | O8: [Write your content here]; O9: [Write your content here] | [Write your content here] |
| WP5 | [AI Research Domain Cluster 4, e.g., AI for Biomedical Science] | 1 | 48 | [INSTITUTION-5] | [INSTITUTION-X, COMPANY-Y, COMPANY-Z] | DC-03, DC-08, DC-10, DC-11 | O10: [Write your content here]; O11: [Write your content here] | [Write your content here] |
| WP6 | Training, Supervision, and Career Development | 1 | 48 | [INSTITUTION-6] | All | All | O12: Deliver all NWTEs; O13: Coordinate IDPs; O14: Monitor DC progress and supervisory quality | [Write your content here — describe NWTE planning and coordination procedures, IDP framework, supervisory monitoring, the role of thesis advisory committees, and the training quality assurance process] |
| WP7 | Dissemination, Exploitation, and Communication | 1 | 48 | [INSTITUTION-1] | All | All | O15: Maximise scientific dissemination; O16: Drive exploitation of results; O17: Communicate with public and policymakers | [Write your content here — describe the dissemination strategy for publications and open software, the exploitation strategy for industrial uptake, and the communication strategy for public engagement] |
Scientific Deliverables
| Del. # | Title | Description | WP | Lead Participant | Type | Dissemination Level | Due Date (month) |
|---|---|---|---|---|---|---|---|
| D2.1 | Open Dataset I: [Domain Cluster 1 benchmark dataset name] | [Write your content here — describe the dataset: domain, size, format, and significance for AI benchmarking in this domain] | WP2 | [INSTITUTION-2] | Dataset (FAIR) | Public | M18 |
| D2.2 | Open-Source AI Toolkit I: [Toolkit name for Domain Cluster 1] | [Write your content here — describe the toolkit: algorithms, models, documentation, and intended user community] | WP2 | [INSTITUTION-2] | Software (open source) | Public | M36 |
| D2.3 | Scientific Report: AI methods for [Domain Cluster 1] — state of the art and network advances | [Write your content here] | WP2 | [INSTITUTION-2] | Report / white paper | Public | M42 |
| D3.1 | Open Dataset II: [Domain Cluster 2 benchmark dataset name] | [Write your content here] | WP3 | [INSTITUTION-3] | Dataset (FAIR) | Public | M20 |
| D3.2 | Open-Source AI Toolkit II: [Toolkit name for Domain Cluster 2] | [Write your content here] | WP3 | [INSTITUTION-3] | Software (open source) | Public | M38 |
| D3.3 | Scientific Report: AI methods for [Domain Cluster 2] — network advances | [Write your content here] | WP3 | [INSTITUTION-3] | Report / white paper | Public | M44 |
| D4.1 | Open Dataset III: [Domain Cluster 3 benchmark dataset name] | [Write your content here] | WP4 | [INSTITUTION-4] | Dataset (FAIR) | Public | M20 |
| D4.2 | Open-Source AI Toolkit III: [Toolkit name for Domain Cluster 3] | [Write your content here] | WP4 | [INSTITUTION-4] | Software (open source) | Public | M36 |
| D4.3 | Scientific Report: AI methods for [Domain Cluster 3] — network advances | [Write your content here] | WP4 | [INSTITUTION-4] | Report / white paper | Public | M44 |
| D5.1 | Open Dataset IV: [Domain Cluster 4 benchmark dataset name] | [Write your content here] | WP5 | [INSTITUTION-5] | Dataset (FAIR) | Public | M18 |
| D5.2 | Open-Source AI Toolkit IV: [Toolkit name for Domain Cluster 4] | [Write your content here] | WP5 | [INSTITUTION-5] | Software (open source) | Public | M38 |
| D5.3 | Scientific Report: AI methods for [Domain Cluster 4] — network advances | [Write your content here] | WP5 | [INSTITUTION-5] | Report / white paper | Public | M44 |
Management Deliverables
| Del. # | Title | Description | WP | Lead Participant | Type | Dissemination Level | Due Date (month) |
|---|---|---|---|---|---|---|---|
| D1.1 | Data Management Plan (DMP) v1 | Initial DMP covering all anticipated research data types, FAIR compliance strategy, data sharing and archiving plan | WP1 | [INSTITUTION-1] | Plan | Restricted | M3 |
| D1.2 | Consortium Agreement | Signed CA covering IP, liability, DC employment conditions, secondment agreements, and governance | WP1 | [INSTITUTION-1] | Agreement | Restricted | M3 |
| D1.3 | Data Management Plan (DMP) v2 (updated) | Updated DMP reflecting actual data generated at midterm | WP1 | [INSTITUTION-1] | Plan | Restricted | M24 |
| D1.4 | Annual Management Report Year 1 | Progress, financial, and risk management report for Year 1 | WP1 | [INSTITUTION-1] | Report | Restricted | M12 |
| D1.5 | Annual Management Report Year 2 | Progress, financial, and risk management report for Year 2 | WP1 | [INSTITUTION-1] | Report | Restricted | M24 |
| D1.6 | Annual Management Report Year 3 | Progress, financial, and risk management report for Year 3 | WP1 | [INSTITUTION-1] | Report | Restricted | M36 |
| D1.7 | Final Management and Coordination Report | Comprehensive final report covering all management activities, lessons learned, and sustainability plan | WP1 | [INSTITUTION-1] | Report | Public | M48 |
| D6.1 | Training Programme Plan | Detailed schedule and content of all NWTEs, IDP framework documentation, and supervisory guidelines | WP6 | [INSTITUTION-6] | Plan | Public | M3 |
| D6.2 | Training Quality Report (Midterm) | Assessment of training quality based on DC feedback and supervisory evaluation at midterm | WP6 | [INSTITUTION-6] | Report | Restricted | M24 |
| D6.3 | Training Quality Report (Final) | Final assessment of training programme quality and recommendations for future networks | WP6 | [INSTITUTION-6] | Report | Public | M46 |
| D7.1 | Communication and Dissemination Strategy | Plan covering target audiences, channels, KPIs, and timeline for all dissemination and communication activities | WP7 | [INSTITUTION-1] | Plan | Public | M3 |
| D7.2 | Network Website and Social Media Channels | Launch of [ProjectAcronym] public website and social media presence | WP7 | [INSTITUTION-1] | Website / online | Public | M4 |
| D7.3 | Midterm Dissemination and Exploitation Report | Inventory of publications, software releases, datasets, patents, and communication activities at midterm | WP7 | [INSTITUTION-1] | Report | Public | M24 |
| D7.4 | Final Dissemination, Exploitation, and Communication Report | Comprehensive final report on all DECs activities; open software/data inventory; exploitation and sustainability plan | WP7 | [INSTITUTION-1] | Report | Public | M47 |
| MS # | Title | Related WP(s) | Lead Participant | Due Date (month) | Means of Verification |
|---|---|---|---|---|---|
| MS1 | Project start: all DCs recruited and employed | WP1, WP6 | [INSTITUTION-1] | M6 | All DC employment contracts signed and active; confirmation from beneficiary HR offices |
| MS2 | Consortium Agreement signed by all beneficiaries | WP1 | [INSTITUTION-1] | M3 | Signed CA submitted to EC Project Officer |
| MS3 | Data Management Plan v1 approved | WP1 | [INSTITUTION-1] | M4 | DMP approved by Steering Committee; submitted as D1.1 |
| MS4 | All IDPs established for all 15 DCs | WP6 | [INSTITUTION-6] | M6 | IDPs signed by DC and supervisory team; deposited in network IDP system |
| MS5 | NWTE-2 (Summer School I) completed | WP6 | [INSTITUTION-2] | M5 | Attendance records; DC feedback report submitted to Training Committee |
| MS6 | First DC publications submitted (preprints) | WP2–WP5 | All | M12 | Minimum 5 preprints posted on arXiv or equivalent open repository |
| MS7 | NWTE-5 (Annual Meeting I) completed | WP6 | [INSTITUTION-5] | M12 | Meeting report and DC presentation abstracts available on project website |
| MS8 | All secondment agreements signed | WP1, WP6 | [INSTITUTION-1] | M9 | Signed secondment agreements for all planned DC secondments on file |
| MS9 | Open Dataset I and II released (FAIR) | WP2, WP3 | [INSTITUTION-2], [INSTITUTION-3] | M20 | Datasets published on Zenodo with DOI and FAIR metadata; announced on project website |
| MS10 | Midterm scientific review by external advisory board | WP1 | [INSTITUTION-1] | M24 | External Advisory Board report received; recommendations addressed by Steering Committee |
| MS11 | NWTE-9 (Annual Meeting II + Midterm Symposium) completed | WP6 | [INSTITUTION-6] | M24 | Meeting report; DC mid-term assessment forms completed |
| MS12 | All four open-source AI toolkits in beta release | WP2–WP5 | Domain WP leads | M30 | Beta releases available on GitHub with documentation; announced on project website |
| MS13 | Open Datasets III and IV released (FAIR) | WP4, WP5 | [INSTITUTION-4], [INSTITUTION-5] | M22 | Datasets published on Zenodo with DOI; announced on project website |
| MS14 | ≥ 30 peer-reviewed open-access publications submitted | WP7 | [INSTITUTION-1] | M36 | Publications list verified against open-access repositories |
| MS15 | All DC thesis defences completed or on schedule | WP6 | [INSTITUTION-6] | M42 | Written confirmation from PhD-awarding institutions of defence scheduling or completion |
| MS16 | Final Network Conference held | WP7 | [INSTITUTION-1] | M35 | Conference programme, attendance list, and proceedings/abstracts book available online |
| MS17 | All four open-source AI toolkits in stable release | WP2–WP5 | Domain WP leads | M40 | Stable releases (v1.0+) on GitHub; archived on Zenodo with DOI; usage documentation complete |
| MS18 | Project completion: all deliverables submitted | WP1 | [INSTITUTION-1] | M48 | All deliverables confirmed received by EC; final financial statement submitted |
| DC # | Recruiting Participant | PhD-Awarding Entity | Start Month | Duration (months) | Secondment Duration (months total) | Non-Academic Secondment Duration (months) | Scientific Domain | AI Innovation Theme |
|---|---|---|---|---|---|---|---|---|
| DC-01 | [INSTITUTION-X] | [INSTITUTION-X] | M1 | 36 | 12 | 6 | [e.g., Structural Biology] | [e.g., Equivariant GNNs] |
| DC-02 | [INSTITUTION-X] | [INSTITUTION-X] | M1 | 36 | 12 | 0 | [e.g., Climate Science] | [e.g., Physics-informed transformers] |
| DC-03 | [INSTITUTION-X] | [INSTITUTION-X] | M2 | 36 | 6 | 6 | [e.g., Materials Discovery] | [e.g., Generative diffusion models] |
| DC-04 | [INSTITUTION-X] | [INSTITUTION-X] | M1 | 36 | 12 | 0 | [e.g., Astrophysics] | [e.g., Simulation-based inference] |
| DC-05 | [INSTITUTION-X] | [INSTITUTION-X] | M2 | 36 | 12 | 12 | [e.g., Drug Discovery] | [e.g., Foundation model fine-tuning] |
| DC-06 | [INSTITUTION-X] | [INSTITUTION-X] | M1 | 36 | 12 | 6 | [e.g., Genomics] | [e.g., Multi-modal attention] |
| DC-07 | [INSTITUTION-X] | [INSTITUTION-X] | M2 | 36 | 12 | 6 | [e.g., High-Energy Physics] | [e.g., Fast surrogate models] |
| DC-08 | [INSTITUTION-X] | [INSTITUTION-X] | M1 | 36 | 12 | 0 | [e.g., Neuroscience] | [e.g., Latent variable models] |
| DC-09 | [INSTITUTION-X] | [INSTITUTION-X] | M2 | 36 | 12 | 12 | [e.g., Earth Observation] | [e.g., Self-supervised multi-modal learning] |
| DC-10 | [INSTITUTION-X] | [INSTITUTION-X] | M1 | 36 | 12 | 6 | [e.g., Chemistry] | [e.g., Reinforcement learning for synthesis] |
| DC-11 | [INSTITUTION-X] | [INSTITUTION-X] | M2 | 36 | 6 | 6 | [e.g., Medical Imaging] | [e.g., Uncertainty-aware deep learning] |
| DC-12 | [INSTITUTION-X] | [INSTITUTION-X] | M1 | 36 | 12 | 6 | [e.g., Ecology] | [e.g., Neural ODEs for dynamics] |
| DC-13 | [INSTITUTION-X] | [INSTITUTION-X] | M2 | 36 | 12 | 0 | [e.g., Fluid Dynamics] | [e.g., Neural operators] |
| DC-14 | [INSTITUTION-X] | [INSTITUTION-X] | M1 | 36 | 6 | 6 | [e.g., Bioinformatics] | [e.g., Geometric deep learning for RNA] |
| DC-15 | [INSTITUTION-X] | [INSTITUTION-X] | M2 | 36 | 12 | 6 | [e.g., Quantum Science] | [e.g., ML for quantum error mitigation] |
Notes on Table 3.1d:
- Duration must be between 3 and 36 months.
- Total secondment duration per DC should not exceed 30% of the DC's contract duration (≤ 11 months for a 36-month DC).
- The non-academic secondment duration is a subset of the total secondment duration.
- If a DC does not have a non-academic secondment, enter 0.
- The PhD-awarding entity must be a beneficiary that awards doctoral degrees (as marked in the Consortium Member Table above).
| Risk # | Risk Description | Likelihood (Low / Medium / High) | Severity (Low / Medium / High) | Affected WP(s) | Mitigation Strategy |
|---|---|---|---|---|---|
| R1 | DC recruitment failure: qualified candidates with combined AI + domain expertise are scarce | Medium | High | WP1, WP6, All scientific WPs | Early launch of recruitment (M-3 before project start); active targeting of MSc graduates from AI and domain science programmes; use of MSCA EURAXESS portal and domain-specific job boards; flexible admission criteria recognising AI or domain background with training to bridge the gap; fallback recruitment from associated partner networks |
| R2 | DC dropout or prolonged sick leave | Low–Medium | High | WP1, WP6, Affected scientific WP | IDP monitoring and early intervention; close supervisor support; contingency budget for recruitment of replacement DC (within MSCA rules); clear procedures for extending DC contract within grant duration |
| R3 | Supervisor departure or capacity reduction | Low | High | WP6, Affected scientific WP | Co-supervision model ensures at least 2 supervisors per DC; successor supervisor identified within beneficiary institution; Steering Committee oversight of supervisory quality |
| R4 | AI research results fall short of objectives (negative or null results) | Medium | Medium | Scientific WPs | Built-in interim assessment milestones (MS10 at M24); early pivoting of research direction allowed within scientific WPs; RAISE networks should normalise and publish negative results as part of open science commitment |
| R5 | Industrial partner disengagement or bankruptcy | Low | Medium | WP1, WP5, WP7 | Secondment agreements with financial and IP terms settled at M3; alternative secondment hosts identified among associated partners; secondment obligations renegotiated if necessary, maintaining training objective |
| R6 | Data access or data licensing issues for AI training | Medium | High | Scientific WPs | Data access agreements negotiated before DC recruitment; FAIR data commitments from data-providing partners formalised in Consortium Agreement; alternative publicly available datasets identified as fallback for each DC project |
| R7 | Compute resource bottlenecks for large-scale AI training | Medium | Medium | Scientific WPs | Access to EuroHPC compute time applied for in advance; institutional HPC resources at multiple beneficiaries provide redundancy; cloud compute budget reserved for burst capacity |
| R8 | IP disputes between academic and industrial partners | Low | High | WP1, WP7 | IP provisions clearly defined in Consortium Agreement at M3; technology transfer offices of all beneficiaries engaged in drafting; pre-competitive research framing for most DC projects; any commercially sensitive DC projects assigned to industrial beneficiaries with clear IP terms |
| R9 | Open-source tools lack adoption by scientific community | Medium | Medium | WP7 | Early community engagement (workshops, tutorials) from M12; tools released in beta from M30 to gather user feedback before final release; integration with established domain-specific platforms and workflows; network with domain-specific user communities |
| R10 | Ethical concerns regarding AI use in sensitive scientific domains (e.g., medical AI, dual-use) | Low–Medium | High | WP1, WP5, WP6 | Ethics review at project start; ethics board includes external ethics expert; domain-specific risk assessments for DC-05, DC-11; training module on responsible AI (NWTE-6) addresses these proactively; alignment with EU AI Act requirements |
[Write your content here]
[RAISE GUIDANCE: Describe the computational infrastructure available to the network for AI training and scientific computing: on-premise HPC clusters at beneficiary institutions, access to EuroHPC petascale systems, cloud agreements, and specialised AI hardware (GPU clusters, TPUs). Quantify available compute resources where possible (e.g., "INSTITUTION-X operates an 800-node GPU cluster providing approximately 10 petaFLOPS of AI training capacity"). Also describe domain-specific infrastructure: telescopes, synchrotrons, sequencing facilities, clinical data repositories, etc. that provide the scientific data for AI development. Infrastructure availability is a key differentiator for RAISE networks.**
[Write your content here]
[RAISE GUIDANCE: Explain why this specific combination of partners is optimal. Describe: (a) the scientific domain complementarities across the consortium — each partner brings unique domain expertise that, combined with the network's AI research, covers the scientific problem space comprehensively; (b) the AI methodology complementarities — different partners contribute different AI expertise (e.g., one partner leads in geometric deep learning, another in Bayesian methods, another in reinforcement learning); (c) the academic-industrial complementarity — academic partners drive foundational AI research while industrial partners provide validation environments, proprietary data, and translation expertise; (d) geographic complementarity ensuring the network serves a pan-European research community and contributes to reducing regional disparities in AI-in-science capacity. Include a brief narrative on how the consortium was assembled and why no critical competence is missing.**
Consortium Complementarity Matrix
| Partner | Primary AI Expertise | Primary Domain Expertise | Unique Infrastructure / Resource | DCs Supervised |
|---|---|---|---|---|
| [INSTITUTION-1] | [e.g., Deep learning, foundation models] | [e.g., Structural biology] | [e.g., 400-node GPU cluster, cryo-EM facility] | DC-01, DC-05 |
| [INSTITUTION-2] | [e.g., Scientific machine learning, physics-informed ML] | [e.g., Climate science, geophysics] | [e.g., Regional climate model infrastructure, EuroHPC allocation] | DC-02, DC-13 |
| [INSTITUTION-3] | [e.g., Generative models, Bayesian deep learning] | [e.g., Materials science, chemistry] | [e.g., High-throughput synthesis robot, DFT compute cluster] | DC-03, DC-10 |
| [INSTITUTION-4] | [e.g., Simulation-based inference, normalising flows] | [e.g., Astrophysics, HEP] | [e.g., Telescope data archive, particle physics simulation pipeline] | DC-04, DC-07 |
| [INSTITUTION-5] | [e.g., Multi-modal learning, self-supervised learning] | [e.g., Biomedical imaging, genomics] | [e.g., Clinical imaging dataset (10M images), sequencing core facility] | DC-06, DC-08, DC-11 |
| [INSTITUTION-6] | [e.g., Reinforcement learning, neural ODEs] | [e.g., Ecology, fluid dynamics] | [e.g., Long-term biodiversity monitoring datasets, CFD HPC facility] | DC-09, DC-12 |
| [COMPANY-1] | [e.g., Applied ML, drug design pipelines] | [e.g., Pharmaceutical R&D] | [e.g., Proprietary compound activity database, ADMET data, in-house wet lab] | DC-05 (co), DC-14 |
| [COMPANY-2] | [e.g., AI for Earth observation, data engineering] | [e.g., Satellite data, geospatial AI] | [e.g., Multi-year satellite imagery archive, GPU inference infrastructure] | DC-09 (co), DC-15 |
[Write your content here]
[RAISE GUIDANCE: Each associated partner must have a clearly defined and substantive role — not merely a nominal one. For each, describe: (a) what specific expertise or resource they contribute; (b) which DCs will undertake secondments with them; (c) what training or scientific activities they participate in; (d) what bilateral agreements (Letter of Commitment, secondment agreements) underpin the relationship. Associated partners linked to a specific beneficiary should be explained in the context of that beneficiary's research activities.**
[Write your content here — or delete this subsection if not applicable]
[RAISE GUIDANCE: If any consortium member is from a non-EU, non-associated country, explain the scientific justification for their inclusion and confirm that their participation costs are borne by them or by a third-party funder, not by the MSCA grant. This section may be left blank or deleted if all participants are from EU Member States or Horizon Europe-associated countries.**
End of Part B1
Submission checklist (remove before submission):
- [ ] All
[Write your content here]placeholders replaced with actual content- [ ] All
[PLACEHOLDER]values in tables replaced with real data- [ ] All
[RAISE GUIDANCE: ...]notes removed- [ ] All institution names, supervisor names, and DC titles populated
- [ ] Table 3.1d: all 15 DC rows have correct duration values (3–36 months)
- [ ] Total body text (Sections 1–3) does not exceed 30 pages when formatted in the EC submission template
- [ ] All tables are properly formatted in the EC Word template (tables converted from Markdown)
- [ ] Part B2 (CVs, letters of commitment, ethics) completed separately
- [ ] Proposal submitted via HORIZON-MSCA-2026-DN-01-01 portal, tagged for RAISE call HORIZON-RAISE-2026-01-03
- [ ] Submission completed before 24 November 2026, 17:00 CET