69 phd-in-embedded-system positions at King Abdullah University of Science and Technology
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
Applicants must have a PhD in Computer Engineering, Computer Science, or Electrical and Computer Engineering, and have published their research in prestigious conferences and journals in related
-
://interfaciallab.kaust.edu.sa ). Expectations: Our goal is to enhance the sustainability of growing food crops and native trees in the desert environment of Saudi Arabia. This necessitates an amalgamation of knowledge and
-
holding a PhD in chemical, environmental or process engineering, to apply for a full-time post-doctoral fellowship position in the field of water desalination, focusing on the development of an artificial
-
startup formation, is highly desirable. We particularly encourage applications from candidates at the Assistant Professor rank, individuals with demonstrated translational research and entrepreneurship
-
implanted systems, digital-assisted tools, and platforms for measuring biomolecular or physiological parameters (e.g., smartphone-based diagnostics) Novel microscale or nanoscale technologies for biomolecular
-
PhD levels). The candidate will teach in the BioE curriculum and is expected to contribute to the current list of core courses and electives. The successful candidate is also expected to provide
-
with publications in the top-tier journals of their respective fields. The successful candidate is expected to establish research activities that contribute to the BioE Program’s research mission
-
: May 26, 2025 Location: Saudi Arabia Company: King Abdullah University of Science & Technology Position Summary KAUST Nanofabrication Core Lab (NCL) is a multidisciplinary laboratory supporting research
-
negotiations with funding agencies, industrial sponsors and academic institutions worldwide. The Principal Awards and Contracts Administrator is not only responsible for drafting and negotiating terms and
-
in simulation, prediction, and analysis of large-scale and complex fluid systems. Special emphasis will be directed toward incorporating high-performance computing, advanced algorithms, machine