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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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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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, 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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the interplay between qualitative and quantitative methods and data. There is a growing focus on novel computational methods such as NLP, machine learning, and AI within the group. Teaching activities in
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applications from researchers specializing in probabilistic and neuro-symbolic AI. Areas of interest include, but are not limited to: • Probabilistic machine learning • Deep probabilistic graphical models
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medical images and other health data. The group develops and evaluates clinically meaningful decision support tools by integrating health data, domain knowledge, and machine learning. Key objectives include
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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
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, of which about 90 are PhD students, and about 40 % of all employees are internationals. In total, it has more than 700 students in its BSc and MSc programs, which are based on AAU's problem-based learning