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Field
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requires overcoming coupled challenges in device switching behaviour, converter design, electromagnetic interference, thermal management, and long-term reliability. This PhD project will investigate advanced
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will have the opportunity to: be supervised by an expert in the field; take the lead on an exciting research project; conduct research in close collaboration with partners in the FWO project; design
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, automated design-space exploration, and cross-technology benchmarking, providing new insights into the co-design of learning algorithms, memory technologies, and neuromorphic hardware architectures for future
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algorithms that exploit communication signals and their reflections to sense surrounding obstacles, nearby UAVs and the environment. The developed methods will combine wireless sensing with digital map
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) that drives innovation for the economy and society in Luxembourg and beyond. With cutting-edge expertise in Natural, Built, Industrial environments, Space, AI, Security and defence technologies. LIST bridges
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 3 months ago
16 Jul 2026 Job Information Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Researcher Profile First Stage Researcher (R1
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², making thermal management one of the key bottlenecks for future electronics. This PhD project aims to address this challenge through the design of novel interlayer alloys, development of multi-material
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of the National Infrastructure for federated learNing in DigitAl health (NINA), a national initiative aimed at advancing privacy-preserving AI infrastructure and analytics for healthcare across
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Electronic System Design 4.6/5.0 Signal Processing for Anomaly Detection CV including information about education background and work experience Transcripts and diplomas for Bachelor's and Master's degrees
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longitudinal within-person models, discrete-time survival analysis, and/or explainable machine learning. Each of the three work packages is designed to result in one publication, together constituting a