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researchers working together to develop better understanding of decision-making processes in the provision of active travel infrastructure. The project will address research questions such as, why are some
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are also eligible to submit nominations jointly with a researcher working at a Fraunhofer institute. For details of the nomination process please see the web page Information for nominators
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estimation of rotating tools. Direct tool wear characterization will be based on optical measurement systems and data processing to achieve wear feature recognition and quantification. Indirect tool wear
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signal processing, with experience in machine learning, deep learning, or AI methods. Familiarity with semantic segmentation, anomaly detection, or pattern recognition techniques is highly valued. Sensor
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process, candidates will be asked to select the ICN2 research group(s) they would like to join. Please visit the profiles of our groups at http://icn2.cat/en/research Requirements: Candidates must fulfil
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open up exciting career opportunities? Are you interested in cable technology and condition monitoring and do you have a strong competence in signal processing and machine learning? As a PhD candidate
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, their achievements and productivity to the success of the whole institution. At the Faculty of Electrical and Computer Engineering, Institute of Fundamentals of Electrical Engineering, the Chair of Biomedical
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materialists, electrical engineers, and computer scientists of TUD, RWTH Aachen and Gesellschaft für Angewandte Mikro- und Optoelektronik mbH (AMO ) in Aachen, Forschungszentrum Jülich (FZJ ), Max Planck
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A Doctoral Research Fellowship in Machine Learning for Critical Healthcare is available at the Faculty of Computer Sciences, Engineering and Economics at Østfold University College (ØUC). The research
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for physiological signal recording. An ability to work independently and collaboratively across disciplines will be essential. Excellent communication skills in English, both written and spoken, are required, and