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. The objective of this PhD project is to develop learning-based methods for maritime tracking and prediction in time- and safety-critical applications. Artificial intelligence techniques will be utilized in order
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. The main aim of this project is to investigate the possibility of using geothermal energy, coming from EGs close to the road, to limit or prevent icing and damage to roads. The specific objectives
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. The objective of the research is to use machine learning methods to find models of ship trajectories and traffic patterns that can be used to detect anomalies and predict into the future. The basis for this is
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practices, and pedagogy, all of which provide a solid foundation for interdisciplinary approaches to knowledge development for the future. The programme's overarching objective is to educate candidates who
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. The applicant must have academic training and practical experience in data science applied to healthcare have experience with mass spectrometry-based proteomic bioinformatic pipelines be proficient in one object
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. More information on the research group can be found here: https://uit.no/research/elsys . The position is for a period of three years. The objective of the position is to complete research training
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National Parks as well as to the Botanic Gardens Conservation Internation (BGCI) Global Conservation Consortium for Erica which is coordinated at UiB. Objectives: The candidate will use integrative
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for Erica which is coordinated at UiB. Objectives: The candidate will use integrative morphological and molecular taxonomic methods to study aspects of the diversity of Malagasy Erica, with particular focus
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regulations that provides both incentives and constraints for the maritime energy transition and emission reduction. The research objective of the PhD is to develop models that capture the interaction between
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of these taxa, hindering correct identification, ecological monitoring, and biotechnological use. Objectives: This PhD project will fill these critical gaps by carrying out the first nationwide, seasonally