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Field
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PhD thesis Edge Intelligence for 6G Networks. The PhD project will deep dive into Edge Intelligence for next-generation (6G) communication systems. The project focuses on integrating Artificial
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training or fine-tuning large-scale models (LLMs, VLMs) in distributed settings. Familiarity with cluster computing environments (e.g., SLURM) and Linux-based workflows. Prior experience in a research lab
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-economic understanding of housing’s impact on health. Simply put, it addresses how housing policies and housing systems can make people sick and generate health inequalities. It develops the political
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systems can make people sick and generate health inequalities. It develops the political economy of housing and health combining insights from literatures in (1) housing and health, (2) political economy of
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. The overall objective is to contribute to next-generation decision support and precision health. At BTH, the PhD project will focus on computational and methodological development, with particular emphasis
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submit a General Application (Instancia General ) through the ULPGC Electronic Headquarters (Sede electrónica de la ULPGC). To submit your application through the ULPGC Electronic Headquarters, you must
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detection and automation. The UMLFF project aims to develop next-generation MLFFs with built-in uncertainty predictions to enable safe, automated active learning and create broad, reliable MLFFs. You will
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for recruitment positions for general criteria for the position. Preferred selection criteria Strong interest and knowledge about energy systems and energy markets. Strong interest in distributed optimization
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fully funded PhD positions in the area of data-driven decision-making for energy communities. Energy communities, where groups of households share renewable generation and a common battery storage system
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department in the ISSA expertise center that develops advanced AI solutions involving AI models, algorithms, implementations, sensors and hardware for small scale edge up to large scale distributed and hybrid