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). Desirable assets are: • Machine learning, deep-learning, artificial intelligence, advanced statistical inference; • A solid record of research activities, including relevant publications in international peer
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data-driven learning and which should remain within structured optimization. In line with AID’s research areas, the project will emphasize knowledge embedding, uncertainty representation, risk-aware
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 3 months ago
related field. A strong candidate will have experience or interest in several of the following areas: Machine learning and deep learning; Natural language processing or neural machine translation; Computer
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skills. Fluent in English, both spoken and written. Willing to learn the Dutch language. TU Delft (Delft University of Technology) Working at TU Delft means contributing to solutions that really make a
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independently. Good communication skills. Fluent in English, both spoken and written. Willing to learn the Dutch language. TU Delft (Delft University of Technology) Working at TU Delft means contributing
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in one or more of the following research areas is desirable: geometric numerical integration, structure preserving deep learning, stochastic differential equations, generative AI, numerical
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of cryptographic implementations and hardware-security countermeasures. Experience with hardware reverse engineering, debugging interfaces, or firmware analysis. Experience with machine learning, deep learning
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implementations and hardware-security countermeasures. Experience with hardware reverse engineering, debugging interfaces, or firmware analysis. Experience with machine learning, deep learning, signal processing
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
of two main parts: Improve methods for automatically aligning ontologies and linking data by leveraging the scalability, approximation, and multi-viewpoint capabilities of deep learning methods. Study
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imaging (crucial) Experience with image segmentation, deep learning, or computer vision. Experience with 3D image processing or inverse problems. Experience with experimental research and data acquisition