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of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
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feature filtering procedure to deal with the large feature set necessary to predict the thermoelectric ZT of a material. - Improve the already existing experimental dataset. - Apply different machine
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conceptual DFT (linear response function, Fukui functions) or QTAIM theory (delocalization index), and their validation on a set of compounds known from the literature - interfacing a MLIP (Machine-Learned
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multidisciplinary experience. Knowledge in applied computer science, particularly in machine learning; in fluid mechanics, especially in hydrodynamics; and in electronics, particularly in instrumentation and
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a team More specifically: - For mission 1: knowledge of signal and image processing, machine learning (PyTorch or TensorFlow + NumPy/SciPy), statistical processing & data and results visualisation
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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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, 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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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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, involving expertise in optics, electronics, image and data processing, chemistry, and biology. With the support of several European funding programs, the team is building a data science and machine learning
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team at the Laboratoire d'Informatique de Grenoble (LIG). GetAlp conducts research in NLP, machine learning, evaluation, and interpretability. The project will be supervised by Maxime Peyrard (CNRS