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The School of Mechanical & Aerospace Engineering (MAE) is a robust, dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE
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. The research may, for example, concern scalable optimization methods, resource‑efficient and robust machine‑learning methods, or optimization‑based and/or learning‑based methods for control. The research may be
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Université Paris-Saclay GS Biosphera - Biologie, Société, Ecologie & Environnement, Ressources, Agriculture & Alimentation | Gif sur Yvette, le de France | France | 6 days ago
, épigénomiques et génomiques. Le projet s'appuie sur un système modèle robuste centré sur Malus sylvestris, l'un des parents sauvages du pommier cultivé, qui a persisté face à des fluctuations climatiques passées
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projects and a joint doctoral training program. Subject The Special Scientist - PhD position DC5 focuses on developing provably robust coding and encryption approaches for securing real-time control systems
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mock-ups in controlled conditions, acquisition of multimodal datasets (XRF and optical spectral imaging), and design of robust preprocessing and cross-modal registration pipelines. This axis provides
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capital budgets. Through strong governance, robust financial processes, and cutting-edge systems, we ensure every dollar is put to its best use, fueling research, education, and transformation. Why Join Us
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relevant surface parameters. This project employs combinatorial gradients, arrays, and patterned surfaces of grafted polymers to generate robust, information-rich substrates for (1) the understanding/control
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capable de couvrir l'ensemble des régimes dynamiques et des plages fréquentielles, de manière robuste, interprétable et exploitable d'un point de vue industriel. La thèse proposera un cadre unifié combinant
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robust, scalable software solution that integrate with subsystems such as VMware, Juniper and Fortinet network controllers, and AWS cloud native APIs. Responsibilities include maintaining and extending
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Related works: [1] Sagawa et al., Distributionally Robust Neural Networks for Group Shifts (GroupDRO) https://arxiv.org/pdf/1911.08731 [2] Liu et al., Just Train Twice: Improving Group Robustness Without