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Field
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accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models, including large
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: Scientific Area: Data Science and Engineering; Electrical and Computer Engineering; Computer Science and Engineering; Artificial Intelligence; Computer Vision; Computer Science; Data Science; or Mathematical
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Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related
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methodologies to support human and AI coordination in optimal decision makings, while contributing to the broader research vision and long-term objectives of the research team. Key Responsibilities: Develop
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, computer vision, and predictive modelling, alongside excellent IT, analytical, interpersonal and communication skills in spoken and written English. You must be able to collect, process and present data with
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hands-on experience in areas such as computer vision, deep learning, multi-modal sensing, robotics, structural health monitoring, or digital twin technologies. (Fresh PhD graduates are welcome
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duties as assigned. REQUIREMENTS: REQUIRED: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong programming skills. Strong background in machine
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generated synthetic representations. The project will also explore machine-learning approaches and efficient imaging strategies, including reconstruction of three-dimensional pore structures from radiography
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representations. The project will also explore machine-learning approaches and efficient imaging strategies, including reconstruction of three-dimensional pore structures from radiography. By linking defect
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are highly motivated, have interests in computer vision and neural networks, and want to both contribute to new advances in a field with real world applications. For more information and how to apply