KAUST is offering a 16-month Master of Science degree in Technology Innovation and Entrepreneurship (TIE). The program includes a six-month internship at a Global Technology Innovation Hub. KAUST provides access to facilities like the Prototyping and Product Development Core Lab, robotics and embedded systems labs. Why it matters: The program aims to prepare students for roles in technology startups and established organizations within the rapidly growing Saudi innovation ecosystem.
MBZUAI welcomed 45 international STEM students to its month-long Undergraduate Research Internship Program (UGRIP), a 32% increase from its second year. The program exposes students to AI research and faculty, attracting applicants from universities like IIT Madras, Harvard, and Yale. Interns worked on AI projects including digital healthcare metaverse prototypes, baby cry analysis, and LLM generalizability. Why it matters: This program helps to foster AI talent and promote the UAE as a global hub for AI research.
MBZUAI's Undergraduate Research Internship Program (UGRIP) is a one-month summer program in Abu Dhabi for promising AI students. Launched in 2023, UGRIP aims to expose undergraduates to AI research and talent development. In 2025, around 60 interns will join the program to work on research projects and receive mentorship from faculty. Why it matters: The program strengthens Abu Dhabi's position as a hub for AI research and attracts global talent.
The KAUST Visiting Student Research Program (VSRP) has hosted over 210 interns since 2014, offering research experience with faculty and facilities. Interns, typically in their baccalaureate or post-baccalaureate studies, stay between three and six months. Several interns, like Mariana Prazeres who researched the Hamilton Jacobi equation, have pursued further studies at KAUST. Why it matters: The VSRP program at KAUST serves as a pipeline for attracting international talent to Saudi Arabia and fostering interest in STEM fields.
MBZUAI hosted 34 undergraduate STEM students from around the world for its inaugural one-month Undergraduate Research Internship Program (UGRIP) in Abu Dhabi. The interns, hailing from countries including the UAE, USA, and India, engaged in ongoing MBZUAI research projects spanning sustainability, education, and healthcare. Interns worked on projects ranging from fake news detection using NLP to tumor diagnosis using computer vision. Why it matters: This program strengthens MBZUAI's position as a global AI research hub and cultivates AI talent within the region.
MBZUAI has placed over 90 master's and PhD students in internships at public, private, and multinational organizations globally. The internships span various AI disciplines, including computer science, CV, ML, NLP, and robotics. Students are undertaking placements at institutions like the Technical University of Darmstadt, Johns Hopkins University, and Microsoft Research Lab – Asia, as well as UAE entities like ADNOC and Dubai Police. Why it matters: This initiative strengthens the UAE's AI ecosystem by providing hands-on experience for students and addressing the growing demand for AI expertise in the region.
KAUST's Saudi Summer Internship (SSI) program hosted 29 undergraduate students from Saudi universities this year, up from just 6 students five years ago. The students were assigned to CEMSE labs for 8-12 weeks, conducting research alongside KAUST faculty. At the end of the program, the students participated in a poster competition to present their work. Why it matters: This program fosters local talent in STEM fields and strengthens KAUST's ties with other Saudi universities.
KAUST Ph.D. student Adel Bibi is researching how to bridge the gap between theory and practice in deep learning, focusing on the mathematical understanding of deep learning models. Bibi is currently interning at Intel in Munich and previously worked on various computer vision problems. He aims to use optimization and mathematics to better understand deep learning models and build better models systematically from theory. Why it matters: This research contributes to the fundamental understanding of deep learning, potentially leading to more efficient and reliable AI systems developed in the region.