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machine learning approaches Collaborating with experimental and clinical research partners Support and preparation of scientific reports and journal articles
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teams from universities, research institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates training, exchanges, and career development
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practical applications, including solving mathematical reasoning problems. The ideal candidate has a strong background in machine learning and an interest in bridging rigorous theoretical insights with
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applied research project on the detection, localization and interpretation of explosion events in underground ammunition storage facilities. The position is embedded in a collaborative project with
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universities, research institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates training, exchanges, and career development for PhD students and
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institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates training, exchanges, and career development for PhD students and postdocs. Learn more
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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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qualitative interviews and analysing quantitative survey data, including collaboration with other project researchers. Continuing your education/training with PhD courses offered by the University of Basel and
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collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL (Prof. Olga Fink). IMOS focuses on the development of intelligent algorithms designed to improve the performance
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experience in machine learning and data-driven modeling. Ability to work independently as well as collaboratively in an interdisciplinary team. Excellent command of English, both written and spoken. Previous