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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
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) enables computations to be performed directly on encrypted data without knowledge of the deciphering key, offering significant potential for privacy-preserving deep learning. However, conventional neural
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Essential skills, knowledge and experience: Experience with machine/deep learning development Data-Centric AI Knowledge Notions of cybersecurity and networks are optional Spoken and written English Desirable
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demonstrated by publications in international venues in machine learning, AI for science, graph learning or related areas Solid expertise in deep learning, with experience in at least one of the following
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, Python, Bash). Good level on machine learning. Good level of written and oral English. Ease in a multidisciplinary environment, taste for teamwork, interpersonal skills. Scientific curiosity
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, computational mechanics, computer science, applied mathematics or similar Strong experience with deep learning, e.g. PyTorch, JAX, TensorFlow, and probabilistic methods Familiarity with graph neural networks
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the creation of high-precision digital twins. Activity 1: Integration of Photometric Stereo in Meshroom - Implement processing nodes for normal field and intrinsic color estimation. - Integrate deep learning
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environments (Gazebo, Unreal Engine, or Unity). You have experience in artificial intelligence (Deep Learning, PyTorch) or embedded systems (ROS2, FPGA/VHDL design). You are curious, show scientific rigor and
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, etc.). Robust AI (knowledge of methods for quantifying uncertainty in deep learning or formal verification methods applied to deep learning) Embedded AI Reinforcement learning, supervised and
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detected at a regional scale. The implementation of advanced InSAR processing chains will provide new insights into the phenomena observed and enrich the databases required for deep learning methods