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) Interpretable machine learning for network adaptation. In this thesis, the student will study how interpretable models and explainable learning algorithms could be used in real cellular networks for safe
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of federated algorithms that enable fair management of the advantages and duties of energy communities in which vulnerable people live. The automatic generation of data models using AI technology will enable
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implementation of security provisioning algorithm in DFL environment Where to apply Website https://sede.uvigo.gal/public/catalog-detail/28364578 Requirements Research FieldEngineering » Communication
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-driven proteomic. Who are we looking for? A motivated researcher with strong programming skills and knowledge of machine learning algorithms, bioinformatics, proteomics, and molecular biology, keen to
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employment agreement of IRTA. IRTA will also pay the fees of the doctoral school when receiving the enrolment. (1) 37.5 hour workweek, with Friday afternoons off Intensive work schedule from 15/06 to 15/09 22
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Insurance. Work-Life Balance and Flexibility with flexible work schedules 28 holidays per year Flexible compensation plan: tax advantages contracting some products (health insurance, childcare, training
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-Life Balance and Flexibility with flexible work schedules 28 holidays per year Flexible compensation plan: tax advantages contracting some products (health insurance, childcare, training, among others
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experience. Support to the relocation issues. Life Insurance. Work-Life Balance and Flexibility with flexible work schedules 28 holidays per year Flexible compensation plan: tax advantages contracting some
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on qualifications and demonstrated experience. Support to the relocation issues. Life Insurance. Work-Life Balance and Flexibility with flexible work schedules 28 holidays per year Flexible compensation plan: tax
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to the project/agreement stated. JOB LOCATION AND SCHEDULE: CEMI UDC FACILITIES – NAVANTIA CITIC,Monday to Fridayfrom 9:00 – 14:00 and 16:00 – 18:00 TASKS TO BE PERFORMED AND THE SUBJECT OF THE CONTRACT The main