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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | about 2 months ago
into the high-performance code hawen (https://ffaucher.gitlab.io/hawen-website/ ) with the support of the development team, enabling their application to real-world applications. The program will be divided
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. This includes research on the optimal lithography process. Here, piecewise interference lithography will be investigated, the so called SMILE-process (Stepped Mask Interference Lithography Exposure). Objectives
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the Ph.D. Our recent works on AI privacy and security: Practical Bayes-Optimal Membership Inference Attacks, NeurIPS 2025, https://arxiv.org/pdf/ 24089 Secure Aggregation is Not Private Against Membership
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-year contract starting September/October 2026 https://www.eu4greenfielddata.eu/ APPLICATIONS OPEN ON Juanary 5th 2026 Are you an aspiring researcher ready to drive the digital and green transition in
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the consequence of selection and the best strategy for recording traits and optimizing selection decisions. For example, breeders must carefully assess risks and the trade-offs of selection for different traits
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on developing biochar as a sustainable feedstock for hard carbon anodes in sodium-ion batteries. In collaboration with Besca AB (https://www.bescacarbon.com/ ), this project explores microwave plasma technology
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beyond. The research aims to investigate all aspects of biogenic dyes, from the identification of dye-producing bacteria and the characterization of their pigments, through the optimization
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to investigate all aspects of biogenic dyes, from the identification of dye-producing bacteria and the characterization of their pigments, through the optimization of their production and application
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: Optimization of Innovative Air Mobility Networks Operating Supervisor: Prof. Dr.-Ing Hartmut Fricke, Chair of Air Transport Technology and Logistics and co-supervised by at least one additional
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(e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as