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for managing smart cities. The team has gained substantial experience in machine learning for road traffic monitoring. They are now keen to thoroughly explore the additional opportunities presented by
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Neural QEM-based Mesh Representation. Nissim Maruani, Maks Ovsjanikov, Pierre Alliez, Mathieu Desbrun. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. [2] VoroMesh: Learning
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Profile The ideal candidate should have: • Knowledge of machine learning, especially neural networks, graph neural networks, or federated learning. • Strong mathematical, optimization, and algorithmic
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machine learning for cybersecurity, current systems remain largely based on pattern recognition and struggle to incorporate contextual reasoning, temporal dependencies, and relationships between entities
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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learning) and image processing, An interest in optical instrumentation and the medical field, Programming and machine learning skills, Ability to collaborate in an interdisciplinary team, Analytical and
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they are mainly based on predetermined rules of behavior chosen by the designer. More recently, methods derived from machine learning provided impressive results. However most are datadriven, meaning
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, Applied Mathematics, or Computational Physics/Chemistry with a strong ML focus. Technical: Deep understanding of Deep Learning (Transformers, GNNs, Auto-encoders). Programming: Proficiency in Python and
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. The successful applicant will develop a predictive pipeline using atomistic modeling and machine learning to identify optimal "seeds" for directing crystal growth, followed by rigorous experimental testing
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, understanding and predicting their thermal conductivity from first principles calculations is very challenging. In this doctoral research project, we plan to use machine learning potentials to investigate