The Rainwater Charitable Foundation is launching the Learning Research Network (LRN), a new Tau Consortium initiative designed to advance primary tauopathy research through meaningful uses of artificial intelligence (AI), collaboration, and shared scientific resources. The initiative supports the development of a more connected, reusable, and AI-enabled research environment that can accelerate discovery across projects, laboratories, and disciplines.

This is an open funding opportunity for researchers and technical leaders who can bring strong scientific questions and innovative AI-enabled approaches to the field. Applicants do not need to be current Tau Consortium investigators. We welcome investigators, engineers, data scientists, computational researchers, technical leaders, and interdisciplinary teams from eligible organizations worldwide.

We’re Looking For

RCF is seeking proposals that use AI in meaningful ways to accelerate discovery, enable new forms of scientific collaboration, and strengthen how data, tools, workflows, and knowledge can be shared and reused. AI should be integral to the proposed approach and provide a clear scientific or collaborative advantage over traditional methods.

Within that framework, projects should address important biological or translational questions in primary tauopathies and demonstrate strong scientific significance. Collaborative proposals are strongly encouraged, though not required, and projects may include up to four participating laboratories or technical teams. Applicants are encouraged to develop outputs that can extend beyond the immediate project, including well-curated datasets, software, AI-enabled tools, workflows, standards, or other reusable resources that can contribute to the Learning Research Network and benefit the broader

Areas of Interest

AI-Ready Data
Generate FAIR, well-curated, AI-ready datasets designed for broad scientific reuse. Projects should support interoperability through appropriate metadata, documentation, and data standards and, where appropriate, develop harmonized or benchmark datasets that benefit the broader research community.

AI-Enabled Collaboration and Workflows
Use AI-enabled approaches to create shared research environments that connect data, analyses, workflows, communications, and scientific knowledge to accelerate discovery and collaboration. Projects may develop reusable AI agents, analysis pipelines, APIs, workflow tools, or other resources that enable efficient sharing of knowledge and scientific workflows.

Strengthening the Neurodegenerative Research Field
Develop persistent, interoperable, and reusable infrastructure, tools, standards, or practices that extend beyond the immediate project and can be adopted by the broader research community. Examples may include knowledge graphs, foundation models, data-sharing frameworks, shared scientific infrastructure, and autonomous or semi-autonomous research workflows.

Funding

Funding is available at up to $250,000 per participating team, with a maximum total project budget of $1,000,000 over 4 collaborating teams. Indirect costs will not be awarded.

Process

Applicants will be selected through a competitive two-stage application process beginning with a Letter of Intent (LOI). LOIs will be assessed for scientific significance, meaningful use of AI, potential contribution to the LRN, and overall fit with the initiative. Selected applicants will be invited to submit a full application. Only invited applicants may submit a full proposal.

The LOI application window will be open from September 21 through October 30, 2026. No extensions or late submissions will be granted or reviewed.

Important Dates

AI Based Application Success Predictor

1. ⭐⭐⭐⭐⭐ Direct relevance to primary tauopathies

This is the single most important predictor.

RCF's medical research program is specifically focused on primary tauopathies, including:

• Progressive Supranuclear Palsy (PSP)
• Corticobasal Degeneration (CBD)
• Frontotemporal Degeneration/Dementia (FTD)
• Pick's disease
• Other diseases driven by pathological tau.

The Tau Consortium's stated mission is to commission research and drug-discovery programs aimed at treating and preventing primary tauopathies.

A competitive proposal should therefore make the disease connection explicit:

Primary tauopathy → important disease mechanism/problem → proposed solution → patient benefit

A generic Alzheimer's or neurodegeneration project with only an incidental tau component would generally have weaker strategic alignment than research directly addressing primary tauopathies.

2. ⭐⭐⭐⭐⭐ High-impact, potentially transformative scientific question

RCF deliberately looks for research that can address critical gaps in tauopathy science.

Its Tauopathy Challenge Workshop, for example, selected only 12 participants from 85 global applications in the 2026 cycle based on the innovation and significance of their research ideas.

The Foundation's Rainwater Prize likewise recognizes outstanding innovation and scientific contributions to tau-related disease research.

Strong proposals should therefore identify:

• A major unresolved biological question.
• A fundamental bottleneck in diagnosis or treatment.
• A mechanism that could change the field's understanding.
• A potentially actionable therapeutic hypothesis.

Success test:

If this project succeeds, what important thing will the tauopathy field be able to do or understand that it cannot do today?

3. ⭐⭐⭐⭐⭐ Clear path toward effective treatments

RCF's mission is strongly translational.

The Foundation's medical research strategy is explicitly aimed at accelerating new diagnostics and treatments, while the Tau Consortium's five-year goals include advancing therapeutics to IND-enabling studies.

Its Sprint-to-Lead program is an especially clear example: it funds drug discovery at the hit-to-lead and lead-optimization stages, with the objective of de-risking candidates for in-vivo proof-of-concept and IND-enabling studies.

A strong proposal should therefore establish a credible chain:

Discovery → mechanism → validation → therapeutic target/candidate → preclinical evidence → clinical development

4. ⭐⭐⭐⭐⭐ Mechanistic depth rather than broad screening alone

This is an unusually important RCF-specific predictor.

The Tauopathy Challenge Workshop explicitly states that broad screening approaches alone without a mechanism connection were not encouraged to apply.

This means:

Weak:
"Screen thousands of genes/proteins to find something associated with tau."

Stronger:
"We identified a defined cellular mechanism controlling tau aggregation/clearance and will test whether manipulating that mechanism alters disease pathology."

The Foundation's recent challenge topics include:

• Tau aggregation and transmission.
• Cellular susceptibility to tau.
• Glial and neuronal loss.
• Metabolism.
• Proteostasis.
• Genetic/epigenetic risk.
• Disease progression.
• Clinical-trial stratification. 

5. ⭐⭐⭐⭐⭐ Strong therapeutic, biomarker or diagnostic potential

RCF's current five-year Tau Consortium goals provide unusually clear evidence about where it wants the field to go.

The Consortium aims to:

1. Transition validated targets into its discovery pipeline.
2. Identify liquid biomarkers for diagnosis/progression.
3. Develop a tau PET ligand with high affinity for 4R tau.
4. Advance two programs toward IND-enabling studies.
5. Strengthen clinical readiness and patient access. 

Consequently, proposals involving:

• Fluid biomarkers
• Neuroimaging
• Tau PET
• Patient stratification
• Disease progression
• Therapeutic targets
• Drug discovery
• Preclinical efficacy
• Clinical trial readiness

have particularly clear strategic relevance.

6. ⭐⭐⭐⭐⭐ Meaningful collaboration and cross-disciplinary expertise

RCF places strong emphasis on collaboration.

The Tau Consortium describes its researchers as scientists who collaborate across disciplines and institutions and actively partner with others to accelerate collective progress.

The Tauopathy Challenge Workshop is specifically structured around bringing researchers from diverse specialties together to address a common problem.

The current LRN opportunity goes even further: collaborative proposals are strongly encouraged, with up to four laboratories or technical teams permitted.

Potentially valuable combinations include:

• Neuroscience + computational biology
• Molecular biology + medicinal chemistry
• Clinical neurology + biomarkers
• Imaging + disease biology
• AI/data science + tau biology
• Structural biology + drug discovery
• Human tissue + animal models

The key is not collaboration for its own sake; the team should demonstrate why the combination is necessary to answer the question.

7. ⭐⭐⭐⭐☆ AI and data-science integration where it provides a genuine advantage

For the 2026 Learning Research Network, this has become a major current-cycle predictor.

RCF states that AI must be integral to the proposed approach, rather than an add-on. Applicants must explain why AI provides a meaningful advantage over traditional methods and demonstrate technical feasibility.

Potentially strong applications may use AI for:

• Image analysis.
• Biomarker discovery.
• Multimodal data integration.
• Disease progression modeling.
• Patient stratification.
• Hypothesis generation/testing.
• Drug discovery.
• Large-scale biological data analysis.
• Reusable computational workflows.

However:

"We use AI" is not sufficient.

The proposal should demonstrate:

Scientific problem → AI advantage → technically feasible implementation → scientifically meaningful output.

8. ⭐⭐⭐⭐☆ Reusable data, tools and scientific infrastructure

The LRN introduces an additional RCF priority that is broader than the immediate scientific question.

Applicants are encouraged to produce resources that can benefit researchers beyond the funded project, including:

• FAIR datasets.
• AI-ready datasets.
• Software.
• APIs.
• Analysis pipelines.
• Workflow containers.
• Standards.
• Reusable computational tools.
• Benchmark datasets.
• Well-documented research resources. 

The proposal should therefore explain:

What will remain useful after the grant ends?

This is particularly important for the current LRN competition.

9. ⭐⭐⭐⭐☆ Strong investigator track record and leadership potential

RCF's selection philosophy places substantial emphasis on researchers capable of making significant scientific contributions.

The Rainwater Prize, for example, considers:

• Scientific contributions.
• Leadership.
• Mentorship.
• Overall contributions to the scientific community. 

The Tau Consortium similarly seeks talented researchers with a proven or high potential ability to make significant impact.

For early-career researchers, this does not necessarily mean having a long publication record. Instead, reviewers should see evidence of:

• Scientific independence.
• Strong first/senior-author work.
• Relevant expertise.
• A coherent research trajectory.
• Ability to lead the proposed work.

10. ⭐⭐⭐⭐☆ Feasibility, development stage and appropriate scope

RCF's programs are often designed around specific scientific-development milestones.

For example, Sprint-to-Lead expects projects to be ready for hit-to-lead or lead-optimization work and requires a path toward pharmacological testing and eventual IND-enabling studies.

The LRN program limits project duration to 12 months, requiring a focused plan and appropriate outputs.

A competitive proposal therefore needs:

• Appropriate preliminary evidence.
• Clear milestones.
• Qualified personnel.
• Access to necessary samples/models.
• Appropriate technical infrastructure.
• Realistic timeline.
• A budget proportional to the work.