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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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competencies The applicant must hold a master’s degree in engineering and a PhD in a relevant field, such as electrical engineering, with expertise in physics-based modeling, machine learning, and optimization
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control venues such as the IEEE Conference on Decision and Control and IEEE Control Systems Letters, and in top machine learning conferences such as NeurIPS, ICML or AAAI, is expected. Proficiency in MATLAB
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, mechanical and durability testing, and integration with advanced machine learning models. The postdoc will collaborate closely with CEBE’s parallel work packages. Experimental and analytical data generated in
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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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stronginterest and experience with GIS data and tools for urban mobility with someprogrammingskills of Python/R, JavaScript, database management environments, Geographical AI and machine learning workflows