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algorithms, and some knowledge of data science and machine learning (through coursework, self-learning, or personal projects). The selected student will work with Ph.D. and master's students to help develop
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
into resources that can be used for machine learning. The PhD will therefore investigate multimodal approaches that connect visual sign-language information with textual representations under low-resource
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related field with a strong quantitative focus. Strong programming skills in Python and demonstrated experience with machine deep learning frameworks (for instance, PyTorch or TensorFlow), preferably
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, or computer science. Core competencies: solid background in quantum many-body physics strong programming skills (Python required, Rust a plus) experience with tensor networks, variational Monte-Carlo, machine learning
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mechanisms that integrate queueing theory, traffic modelling, machine learning, and network-performance prediction for improving latency, reliability and fairness to support mission‑critical services
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
(or surrogate models) are approximations of classical numerical solvers with a very low computational cost. They form the core of a digital twin. Using machine learning techniques to build these meta-models
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, or supervised/unsupervised learning depending on the available data) using spatial analysis and geographic machine learning tools (e.g., scikit-learn, PyTorch/TF + GeoPandas/Shapely) - Implementing a semantic
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WDM switches and the fast control to enable novel low latency highly scalable and flat interconnect AI compute clusters. Machine learning clusters and artificial intelligence (AI) training have become
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, calorimetry, and synchrotron experimental measurement techniques. Knowledge of AI-based and machine-learning methods is also beneficial. For further information about a specific subject see General syllabus