G42 and the Government of India formalized a commercial framework for the deployment of Condor Galaxy India, an 8-exaflop AI supercomputing cluster comprising 64 Cerebras CS-3 systems. This agreement, witnessed by the UAE President and Indian Prime Minister, positions G42 to partner with India's Centre for Development of Advanced Computing (C-DAC) for installation and operations. The supercomputer is intended to be a foundational asset for India's sovereign AI ambitions and underpin joint R&D across health, genomics, energy, and geospatial analytics. Why it matters: This major infrastructure partnership significantly advances AI capabilities in India, deepens strategic ties between the UAE and India, and expands G42's global footprint in high-performance computing.
Researchers from King Abdullah University of Science and Technology (KAUST), in collaboration with Universidad de los Andes and others, investigated mangrove ecosystems for enzymes capable of degrading plastics like PET. They discovered that adding agricultural residues to mangrove soils increased the number of potential PET-degrading enzymes and identified a previously unknown group of salt-tolerant enzymes. The team employed metagenomics, artificial intelligence, and 3D structural analysis to study these enzymes, publishing their findings in Nature Communications. Why it matters: This research offers potential new enzymatic solutions for global plastic waste management, particularly for high-salinity industrial applications, by leveraging the unique biodiversity of environments like Saudi Arabia's Red Sea mangroves.
KAUST and the International Maize and Wheat Improvement Center (CIMMYT) have signed an MoU to collaborate on developing climate-resilient food crops. The collaboration will combine CIMMYT’s expertise in maize and wheat breeding with KAUST’s strengths in genomics and computational agriculture. The partnership will focus on genomic selection, data analytics, and digital breeding technologies, including capacity-building programs. Why it matters: The partnership aims to enhance food security in Saudi Arabia and the wider region by developing resilient, high-yielding crop varieties suited to arid environments.
A KAUST-led study has revealed a unique microbial ecosystem in the Hatiba Mons hydrothermal vent fields of the Red Sea, first documented in 2023. Using genome-resolved metagenomics, the study reconstructed over 300 microbial genomes from five vent sites. The analysis showed an ecosystem dominated by microbes capable of iron, sulfur, nitrogen, and carbon cycling, unlike most hydrothermal vents that are sulfur- and methane-based. Why it matters: The discovery provides new insights into microbial processes in extreme conditions, ocean resilience, and global carbon cycling, highlighting the interplay between geology and biology in the Red Sea.
KAUST and Janssen Pharmaceutical (Johnson & Johnson) are collaborating to research and innovate in neglected tropical diseases, focusing on dengue fever. They signed an MoU to establish a dengue fever research program and an infectious disease R&D center at KAUST's National BioPark. The partnership marks Johnson & Johnson's first university collaboration in Saudi Arabia. Why it matters: This collaboration signifies a major step in localizing disease research within Saudi Arabia and leveraging KAUST's AI, genomics, and data simulation expertise to address global health challenges.
MBZUAI and the Department of Health – Abu Dhabi (DoH) have signed an agreement to collaborate on AI research across genomics, precision medicine, robotics, and AI-powered diagnostics. Signed on October 13, 2025, the MoU aims to drive breakthroughs in disease prevention, early detection, and therapeutics. The partnership seeks to translate AI research into tangible improvements in patient care within Abu Dhabi and beyond. Why it matters: This partnership signifies a major push to establish Abu Dhabi as a leader in biomedical research and AI-driven healthcare innovation.
MBZUAI researchers have developed a new kernel-based method to identify dependence patterns in data, especially in small regions exhibiting 'rare dependence' where relationships between variables differ. The method uses sample importance reweighting, assigning more importance to regions with rare dependence. Tested on synthetic and real-world data, the algorithm successfully identified relations between variables even with rare dependence, outperforming traditional methods like HSIC. Why it matters: This advancement can improve data analysis in fields like public health, economics, genomics, and AI, enabling more accurate insights from complex observational data.
MBZUAI and the Weizmann Institute of Science have launched a joint AI Research Program. The program will foster collaborative projects, training, and exchanges in AI fields like machine learning, computer vision, and NLP. The initiative aims to advance both basic and applied AI research, particularly in healthcare and genomics. Why it matters: This partnership signifies growing international collaboration in AI research within the UAE and could accelerate breakthroughs in key AI applications.