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Field
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prepared for changes to your work duties after employment. Required selection criteria You must meet the requirements for admission to the Doctoral Programme in Computer Scienc e, see Section 6-1 of the PhD
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selection criteria Knowledge of sensors and measurement techniques Experience from the industry providing sails for merchant ships Experience with Computational Fluid Mechanics Knowledge of machine learning
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools
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://www.academictransfer.com/en/jobs/363733/phd-candidate-multimodal-and-ag… Requirements Specific Requirements Profile We welcome candidates with an MSc or equivalent expertise in artificial intelligence, medicine, computer or
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Fundacio de Recerca Clinic Barcelona-Institut d'Investigacions Biomediques August Pi i Sunyer | Spain | 6 days ago
or ELISA. Scientific curiosity, eagerness to learn, and strong teamwork skills will be highly valued. Research group: Cellular Immunotherapies for cancer, Sònia Guedan Project: Next-Generation CAR-T Cells
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into the mechanisms governing hydrogen-induced degradation and failure of circular steels. As a PhD researcher, you will: Perform advanced 3D microstructural characterization of circular steels, focusing on segregation
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. Our community is open and informal, with more than 7,000 students, 1,000 PhD students, and 1,400 staff members from all over the world. If you would like to learn more about the Faculty of Science and
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modern machine learning, statistical signal processing, or optimisation to turn heterogeneous knowledge (channel/network state, maps and topology, mobility, hardware constraints, and task-level KPIs