21 parallel-computing-numerical-methods-"Prof" Postdoctoral positions at King Abdullah University of Science and Technology
Sort by
Refine Your Search
-
/Online. A project at the Composites Lab is characterized by the amalgamation of experimental and computational/modeling mechanics and encompasses people with very different backgrounds to ensure we capture
-
-based precursors. · Explore innovative methods to enhance material properties for energy storage applications and other emerging technologies. · Conduct detailed structural, chemical, and
-
of aquatic foods across time and space (using global and regional datasets). Contribute to the design and implementation of a long-term monitoring program, including: Sampling aquatic foods for nutrient
-
of bioinformaticians, computer scientists, biotechnologists, biologists, and biochemists. The successful candidate will also enjoy an environment aimed to facilitate progress in the research career: networking, student
-
on the development of new methods integrating a variety of data types (remote sensing, geology, geophysics, geochemistry) for geological modelling and advanced exploration targeting of mineral deposits
-
doing so, we will also establish a long-term, market-based monitoring program, generating real-world data to inform smarter, fairer, and more sustainable management of reef fisheries worldwide. We
-
experience in agronomy, soil science, engineering methods, and precision agriculture in semi- and hyper-arid regions. Specifically, we will be testing the effects of soil amendment technologies pioneered by
-
integration methods for the different data types. In terms of applications, the candidate will be free to choose their own case study(s). Additionally, close collaboration with other group members is expected
-
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
-
research in the field of machine learning, more specifically, deep learning and representation learning architectures. Application areas of ML include, but are not limited to, computer vision, natural