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Field
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sanitation industries. Working with our established industry partners, you'll implement your innovations in real operational environments, seeing your research make tangible difference while building
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28 Aug 2025 Job Information Organisation/Company CNRS Department Laboratoire d'informatique de modélisation et d'optimisation des systèmes Research Field Computer science Mathematics » Algorithms
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language. - While our current digital infrastructure relies on classical networks, quantum networks are slowly becoming a reality. The coordination algorithms that govern their operation are unlike those
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremen, Bremen | Germany | about 1 month ago
functions to detect ecosystem change and predict ecosystem characteristics under different impact scenarios. The integrated analysis of marine microbial eDNA data and contextual environmental information with
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on classical networks, quantum networks are slowly becoming a reality. The coordination algorithms that govern their operation are unlike those employed in classical networks, necessitating novel verification
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accurate, well-characterized methods that serve as traceable standards for biomarker quantification, enabling reliable and reproducible measurements across different assays. In this PhD project, you will
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of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised learning techniques (e.g. random forest (RF
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Join TU Delft and work together with NXP to build low-power AI accelerators for self-healing analog/RF calibration, fixing noise/offset. Co-design algorithms & hardware and validate on real silicon
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optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware
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the project you will work with different types of water, ranging from surface water to wastewater treatment plant effluent. The work will integrate hands-on monitoring with cutting edge data-driven and physical