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Field
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events. Maintain and perform analysis on large quantitative datasets; develop and implement statistical or machine learning models to recover patterns of technology adoption, task organization and skill
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written), time management, project management, and presentation skills Proficient with computer applications and programs associated with the position (i.e. Microsoft Office suite) Strong attention
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method development and DNA library preparation for Oxford Nanopore sequencing. Large experience in bioinformatics, machine learning and high-performance computing. Furthermore, excellent written and oral
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main PhD focus) such as additive manufacturing, advanced/hybrid manufacturing, machine learning, artificial intelligence, computer vision, robotics, UAVs, etc. is a plus. Other preferred qualifications
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of working hours Professional equipment available (videoconferencing, loan of computer equipment, etc.) Social, cultural and sports events and activities Access to vocational training The candidate can start
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computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate hydraulic flood
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deep learning / computer vision / biomedical image computing Excellent English; German is an advantage for clinical collaboration We offer Highly interdisciplinary, translational research environment
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development in large multi-site cohorts; and 3) developing advanced machine learning models of task-related brain function. The fellow will have the opportunity to contribute to these projects and will also be
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metagenomics and Oxford Nanopore Technologies sequencing. Large experience in bioinformatics, machine learning and high-performance computing in relation to microbial metagenomics and analysis of horizontal gene
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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