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: max 10 Minimum score to pass the selection process: 60 Selection process A first curricular phase and, if necessary, a second phase of interviews To validate this request, you must validate the form
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, hardware setup, calibration and data collection. Maintain accurate experimental records and document results in a reproducible format. Process and analyse spectroscopic and imaging data using quantitative
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. • Participation in co-creation and validation processes with institutional and social actors, and in the writing of scientific articles and technical reports. • Coordination of WP3: planning, calendars, monitoring
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?categoria.id=821699 Link to the application process: http://www.ub.edu/caiac/solBeca?idConvocatoria=50357 Where to apply Website http://www.ub.edu/caiac/solBeca?idConvocatoria=50357 Requirements Research
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platform: -CV - Research Statement -Publication list -2 Reference letters How to submit your application The application process will only be handled through MathJobs: For further questions, please email
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the following documents through the Mathjobs platform: -CV - Research Statement -Publication list -2 Reference letters How to submit your application The application process will only be handled through MathJobs
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the selection process: i) experience with high-resolution mass spectrometry (HRMS) for analysis of suspect and non-target substances and related software; ii) knowledge in nature-based solutions for wastewater
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to travel to Japan (or elsewhere) for archival and fieldwork. Must be able to conduct independent research in the required primary languages. Selection process Applications will be accepted until 20thJanuary
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-pilot level of solid-state fermentation processes for the valorisation of vineyard and winemaking waste as well as carrying out bibliographic searches in this field. - Develop technical coordination tasks
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow