GEORGIA INSTITUTE OF TECHNOLOGY
- Country
- United States
- Type
- Beneficiary
- Grants received
- 19
Funding record
- 19 grants on record
- 2015–2025 span of the record
Grants recorded here are the ones our sources publish, and the total is the sum of those. It is not a statement of this organisation's total funding.
Programmes
11 further grants are on record without a programme named by the source, or under a programme outside the largest shown here.
Frequent partners
Organisations this one has been funded alongside on two or more projects. A single shared consortium is not counted: the largest here has 195 members, and being on one list together is not a collaboration.
- KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY
- KOKURITSU DAIGAKU HOJIN KYUSHU DAIGAKU
- THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
- UNIVERSITAT POLITECNICA DE CATALUNYA
- BAYERISCHE FORSCHUNGSALLIANZ BAVARIAN RESEARCH ALLIANCE GMBH
- DEAKIN UNIVERSITY
- FLUXIM AG
- HELSINGIN YLIOPISTO
Calls from this organisation
No published calls from this organisation.
Grants received
- Bringing epigraphene nanoelectronics to life
- Enabling the integration of multi-functional reliable metamaterials in real-life industrial applications for enhancing structural and vibrational behavior
- Creativity in Virtual Reality
- INNOVATIVE EUTECTOGELS FOR EMERGING APPLICATIONS
- CO2 Geological Storage: Mineralization in mafic rocks
- - addressing the challenges of high-performance solution-processed OLEDs using sustainable materials
- Lymphoid Organoids to Study Immune Synapse in Lymphoma Therapies
- Building European Nuclear Competence through continuous Advanced and Structured Education and Training Actions
- NAno SCintillator ARrays (NASCAR) as a Novel Nuclear Detection Material
- Regulation of mechanotransduction through motor-molecules activation of focal adhesion kinase in progressive fibrosis
- Brain-inspired technologies for intelligent navigation and mobility
- aRTIFICIAL iNTELLIGENCE for the Deaf
- IONGELS: FROM NEW CHEMISTRY TOWARD EMERGING APPLICATIONS
- Using the smart matrix approach to enhance TADF-OLED efficiency and lifetime
- Machine learning for Advanced Gas turbine Injection SysTems to Enhance combustoR performance.
Data source
© European Union, 2026. Source: CORDIS. Reused under Commission Decision 2011/833/EU — CC BY 4.0.
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