57 phd-in-computer-vision-and-machine-learning positions at King Abdullah University of Science and Technology
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capillary pressure, and data-driven, physics-driven machine-learning. Applications are sought for a two-year postdoc position, and will work closely with an industry partner. The position will include a
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, digital twins, and machine learning into process systems of industrial and societal relevance. Furthermore, we expect research proposals to align closely with the UN Sustainable Development Goals and to
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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
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devices while managing complex imaging datasets and integrating with machine learning systems. What You'll Do Build responsive web applications that work across laptops, workstations, tablets, and mobile
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Bioinformatics (generative protein design) Methodology (machine learning, deep learning, and AI) for analysis and prediction of genotypic variation Methodology (machine learning, deep learning, and AI
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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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The VCC center at KAUST is looking for research scientists in Prof. Wonka's research group. The topics of research are computer vision, computer graphics, and deep learning. A suitable candidate
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The Statistics (STAT) program in the Computer, Electrical, and Mathematical Sciences and Engineering Division (https://cemse.kaust.edu.sa) at King Abdullah University of Science and Technology
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assigned by management. Person Requirements Competencies Technical Skills Knowledge of the airlines' Global Distribution System (GDS). Proficient computer skills, particularly in the MS Suite applications
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. 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