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offered provide access to challenging and interdisciplinary projects integrated into large national and European research consortia such as the DAPHNE (DAta for PHoton and Neutron Experiments) NFDI
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monitoring, and autonomous systems. However, most advances rely on large datasets and computationally intensive architectures that are impractical for scenarios constrained by limited data and resources
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with the world’s highest volcanic activity, yet eruption histories, geological maps, and monitoring data are incomplete or absent. Volcanoes capable of very large magnitude eruptions (M ≥ 6), with
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
generation of wireless communication (6G) to extend network coverage, supporting diverse data-intensive applications such as immersive extended reality and autonomous systems. However, aerial 6G networks will
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large-scale international facilities (such as synchrotron and neutron sources). Details of the research activities as well as publications and background information are available on the group website
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4 Dec 2025 Job Information Organisation/Company Grenoble INP - Institute of Engineering Department Engineering Research Field Engineering Researcher Profile First Stage Researcher (R1) Positions PhD
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. This is further hindered by a lack of diverse big data resources and matched, powerful analytical tools. As a result, progress in the field has been characteristically slow over the last 50 years
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you will have access to the supercomputing facility at Umeå University, and to unique large-scale data infrastructure (www.umu.se/en/research/infrastructure/umea-simsam-lab/ ) covering the whole Swedish
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barriers: a large input modality gap, as network data consists of diverse, non-textual formats like time-series metrics, graphs, and scalar values; the inefficiency and unreliability of answer generation
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), machine learning (ML), deep learning (DL) and Data science methods for medical image analysis, to autonomously grade the fundus images from large datasets. This will be supported by Professor Neil Vaughan