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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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conventional simulators. Finite element-based methods such as Mixed-Finite-Element or Control-Volume methods are convenient thanks to their suitability for complex unstructured grids. Applications are sought
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This is a unified application form for all positions in the Beyesian Deep Learning group at KAUST led by Prof Maurizio Filippone, including Research Intern MS/PhD Student PhD Student Postdoctoral
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Application documents: 1) Brief cover letter, explaining your motivation for applying, 2) Detailed curriculum vitae (including your email address), 3) Complete transcript of grades from all your university-level studies. We do not ask for more information/documents at this point (but you can...
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and autonomously work on scientific and/or operational impact project(s) in collaboration with KAUST faculty member or with teams in the lab or other core labs, in the framework of method development
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research partners. Support the supervision of PhD and MSc students.
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Position Summary The Graduate Program Student Advisor (GPSA) in the Biomedical Sciences Division oversees and manages the entire graduate student population, including both MS and PhD students
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focuses on analytical work, particularly near-infrared (NIR) spectroscopy, including the development of NIR methods for coffee and other fruit crops. The remaining 20% supports the programme’s in vitro
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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
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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