Sort by
Refine Your Search
-
topics. Candidates should have some experience working with FPGAs as well as an understanding of computer networks. Experience with both RTL and HLS design is favoured. The ideal candidate would have some
-
-performance SWIR and exploratory room-temperature MWIR detection. Candidates should hold a PhD in chemistry, materials science, electrical engineering, applied physics, or a related field, with expertise in
-
areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
-
are able to offer. See the different category headings below to find out more or change your settings. You may also be able to exercise your privacy choices as described in our Privacy Policy
-
manipulation for high-performance SWIR and exploratory room-temperature MWIR detection. Candidates should hold a PhD in chemistry, materials science, electrical engineering, applied physics, or a related field
-
are able to offer. See the different category headings below to find out more or change your settings. You may also be able to exercise your privacy choices as described in our Privacy Policy
-
degradation, and connect them to cell- and pack-level safety performance. · Design model-based and data-driven frameworks for battery management systems, including health monitoring, early fault detection, and
-
universities in the world, KAUST intends to become a major new contributor to the global network of collaborative research. It will enable researchers from around the globe to work together to solve challenging
-
Zamborain-Mason, and will collaborate closely with an exceptional network of researchers and practitioners, including Professors Joshua Cinner (University of Sydney), Christina Hicks (Lancaster University
-
. These workflows will then be applied in relevant Saudi Arabian contexts to help discover new ore deposits. The position will combine techniques from geological modelling, geostatistics, machine learning, and