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of the PhD regulations for more information. You must have a master's degree or equivalent in computer science, artificial intelligence, machine learning, computer vision, or signal processing. The degree
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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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at the intersection of computer vision, micro-electronics analysis, and hardware security, and will work under the supervision of researchers within the Department of Intelligent Systems. The PhD researcher will be
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Join us in developing machine-learning accelerated simulation methods to understand and optimize interfaces in hybrid organic-inorganic materials for sustainable energy devices. Your work
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deep-learning and 3D computer-vision models that detect features while representing a distribution of plausible interpretations. Encode geological relationships in a knowledge graph that stores
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CAD tools Finite-element analysis and simulation Rapid prototyping and additive manufacturing Signal processing and data analytics, control systems Machine learning or computer vision Product
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into the transmission X-ray imaging regime. The developed techniques will be validated on real data. As a candidate, you must have a strong background in machine learning, computational imaging, and/or computer vision
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Signal processing and data analytics, control systems Machine learning or computer vision Product development in multidisciplinary engineering teams. Responsibility teams Exposure to globally leading
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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
conditions, using techniques from computer vision, natural language processing, and representation learning [4,5]. A third objective will be to study how sign-language videos can be represented in a way that