Google and Tel Aviv University deepen AI partnership with $1M for foundational research and student support

Google and TAU will extend their AI partnership through 2028 with $1M and shared compute. Focus: efficient ML, multilingual/multimodal evals, privacy, quantum, and climate.

Categorized in: AI News Science and Research
Published on: Nov 28, 2025
Google and Tel Aviv University deepen AI partnership with $1M for foundational research and student support

Deepening our AI research partnership with Tel Aviv University

Google and Tel Aviv University (TAU) are expanding a long-running research partnership with a new three-year engagement for 2026-2028, backed by $1 million from Google.org. The goal: accelerate foundational AI research, enable ambitious joint projects, and invest in the local research ecosystem.

Since the partnership formally began in 2020, Google Research and TAU's Center for AI and Data Science (TAD) have collaborated on AI for Social Good (launched in 2021) and AI for Sustainability and Education (launched in 2023). More than 20 projects have landed across education, multimodal learning, and environmental applications.

What's been achieved so far

  • Studying educational values in large language models and how they surface in real tasks.
  • Exploring links between 3D neural representations and language.
  • Applying AI to improve wastewater treatment and environmental monitoring.

Focus areas for 2026-2028

The next phase centers on high-impact questions that matter to labs and industry teams alike. Funding, grants, and joint work will prioritize:

  • Efficient and sustainable ML: Algorithmic advances that reduce compute and energy cost while preserving accuracy.
  • Multilingual, multimodal, multicultural AI: Better evaluation methods to assess how models reason across languages and image-text inputs.
  • Next frontier of computing: Novel quantum algorithms aimed at practical use cases.
  • Safe and privacy-preserving AI: Techniques that improve privacy, security, and reliability in deployment.

Collaborative projects starting now

New joint efforts between Google Research teams and TAU researchers include Evaluations for GenAI, and AI for Climate & Weather Research. Early targets:

  • Using ML to build the first global map of flower color to support biodiversity and conservation.
  • Developing generative navigation methods for embodied agents.

To support compute-heavy work, researchers will receive Google Cloud Platform (GCP) credits. This also enables direct experiments with Google's latest open models, such as Gemma, within secure research workflows.

Building the next generation of researchers

The partnership is integrating AI and Data Science courses into fields like Law, Humanities, and Life Sciences, giving students tools to answer questions that previously stalled out. Programs like BITS of AI will expand early training and mentorship.

Funding will support PhD scholarships and travel awards so researchers can present work globally. Two TAU PhD students were also awarded fellowships through the Google PhD Fellowship program. Additionally, students at Israeli universities will receive one year of a Google AI Pro plan at no cost to broaden access to modern AI tools.

Why this matters for your lab

  • Lower training and inference costs: Efficiency research can turn previously infeasible experiments into weekly runs.
  • Stronger evaluations: Multilingual and multimodal benchmarks reduce blind spots and help compare methods fairly.
  • Privacy by design: Techniques that minimize sensitive data exposure while maintaining performance.
  • New compute paradigms: Quantum algorithms tested against real problems, not toy demos.
  • Climate and biodiversity impact: Datasets and methods that tie directly to conservation and weather research.

How researchers can engage

  • Watch for calls from TAU's TAD and partner labs for grants, joint proposals, and student support.
  • Plan evaluation-first studies with multilingual and multimodal metrics baked in from day one.
  • Design for compute realism: track energy, latency, and cost alongside accuracy.
  • Adopt privacy-preserving methods early (e.g., data minimization, secure evaluation pipelines).
  • Prioritize reproducibility with clear datasets, baselines, and open artifacts where possible.

Resources

The bottom line

Google and TAU are scaling a proven collaboration with new funding, shared infrastructure, and a tighter focus on efficiency, evaluation, privacy, quantum algorithms, and climate research. If your work intersects these areas, this is a strong window to propose ambitious projects and help move the science forward.


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