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programme Materials Science, Mechanical and Manufacturing Engineering, is open for appointment from 01.04.2026 or soon thereafter. The position is available for a period of 3 years. Job description The PhD
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the European Research Council project REVIVE, combining experimental, analytical, and computational approachesofbatteries.Thismultidisciplinaryandhigh-visibilityenvironmentprovidesexcellent opportunities
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obtainable using the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. Furthermore, the postdoc will aid in
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obtainable using the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. This postdoc position will utilize
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the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. The postdoc will be part of the Microbial Metagenomics group
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. The work includes theoretical analysis, algorithm design, and validation on realistic case studies, as well as dissemination of results through publications, open-source code, and collaboration with internal
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algorithms for physiological data Development of mobile applications for sensor integration and patient use Support in setting up cloud-based infrastructures for secure data collection and storage
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algorithms for speech enhancement using state-of-the-art machine learning techniques. You will design and evaluate models that leverage phoneme-level or discrete speech representations and conduct experiments
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characterization of lithium-ion and post-lithium-ion batteries, as well as solid oxide electrolysis cells (SOECs). The role also includes developing performance models and state-of-health estimation algorithms
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, artificial intelligence (AI), machine learning, and computation have emerged as powerful digital technologies for creatively generating new design ideas and rapidly advancing formgiving methods within