Joint research
Defined research questions, shared evaluation protocols and pathways to real-world validation.
Academic Partnerships / Europe
Research talent.
Real-world systems.
We invite universities, laboratories, researchers and students to connect scientific work with the engineering realities of Physical AI.
01 / Purpose
Europe can turn research excellence into physical intelligence.
Robots need more than models. They need grounded data, reliable evaluation, safe interaction and learning systems that survive contact with the real world.
GappAI Academic Partnerships is an invitation to explore well-scoped collaboration between academic research and applied engineering. We seek work that creates knowledge while moving credible robotic capability forward.
Universities / Laboratories / StudentsScientific questions.
Scientific questions.
Physical systems.
02 / Collaboration formats
Thesis projects
Bachelor's and master's projects with university supervision and an industry-side technical advisor.
Student opportunities
Paid working-student and internship roles connected to active engineering needs, subject to availability.
Research residencies
Time-bounded collaborations for researchers working on difficult, measurable Physical AI problems.
03 / Research directions
Problems worth
solving together.
Projects should be technically rigorous, operationally relevant and suitable for responsible evaluation.
01Human-to-robot transferDemonstration learning, multimodal data and reusable skills.
02Perception & manipulationVision, touch, force and tool use under uncertainty.
03Adaptive autonomyRecovery, escalation and long-horizon task execution.
04Safety & evaluationRisk-aware deployment, benchmarks and evidence of reliability.
Human expertise / Instrumented learningResearch becomes data.
Research becomes data.
Data becomes capability.
04 / A responsible model
Learning and contribution must work both ways.
Student participation is structured around education, mentorship and clearly defined technical contribution—not unstructured labour.
- 01Clear project scope and expected learning outcomes
- 02Technical mentorship and university supervision where required
- 03Fair compensation or academic credit appropriate to the format
- 04Written expectations for data, confidentiality, publication and intellectual property
- 05Safety, research integrity and responsible robotics throughout
Universities / Laboratories / Researchers
Propose a focused
collaboration.
Tell us the research question, participating institution, proposed format, supervision model and the physical system or dataset involved.
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