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, 2026 Description This is a unified application form for all positions in the Optimization and Machine Learning Lab at KAUST led by Prof Peter Richtarik, including Research Intern MS/PhD Student PhD
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cutting-edge machine (deep) learning numerical methods for wave-equation-based processing, imaging, and inversion. Wave phenomena are ubiquitous in science, and they extend to objectives ranging from
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areas: computer vision, computer graphics, and deep learning. We require documented experience about computer vision, machine learning, deep learning or optimization, demonstrated by high-quality
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Technology (KAUST) is looking for postdoctoral researchers in the area of computer vision, AI, and machine learning. The initial appointment will be for 2 years with a possible extension with a
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machine learning are desirable. Application Instructions Applications must be submitted through PlutoEdu ( https://www.plutoedu.com/form/792303137 ) Starting date As soon as possible, ideally within Q4 2025.
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techniques from geological modelling, geostatistics, machine learning, and potentially geophysics (inversion) to develop new exploration targeting methods. These will be designed to make the most of the
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mineral resources Expertise in predictive or machine learning-based spatial modeling in a geoscience context Experience with the operation of deep marine research instruments at sea Application
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opportunities for social, sporting, and learning activities outside of work. Qualifications Basic Qualifications PhD in Earth Sciences, Geosciences, Geochemistry, Mineralogy or a closely related relevant field
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+ nationalities) Qualifications Core Requirements PhD in Computer Vision, Medical Imaging, Applied Machine Learning, or a closely related field. Hands-on experience with generative models — diffusion (DDPM/LDM