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Field
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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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the area of quantum and hybrid classical-quantum networks regarding their design, management, and debugging in distributed architectures. Well-Planned Interdisciplinary Training: To sustainably train PhD
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distributed MIMO systems. Your work assignments The research focus for the advertised position is machine learning for telecommunications. The position is part of the project "Machine learning for sensing in
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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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for next-generation (6G) communication systems. The project focuses on integrating Artificial Intelligence (AI) and Machine Learning (ML) capabilities into distributed cloud-edge infrastructures to enable
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, populations, and types of amenities remains largely unexamined. Why 15, rather than 10, 20, or 30 minutes? The assumption that the health and wellbeing benefits of this model will be evenly distributed is
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the distributions and stable isotopic composition of plant waxes and the relative abundance of bacterial membrane lipids (brGDGTs). The PhD students will integrate their results with complementary climate proxies and
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HBIGS Heidelberg Biosciences International Graduate School | Heidelberg, Baden W rttemberg | Germany | 9 days ago
30℃) conditions, using PHYB immunostaining, SABER-FISH labelling of PHYB target loci (S1), 3D STED imaging and available image analysis workflows2 (S2). (Obj2) We will measure the spatial distribution
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manufacturing machines and advanced mechatronic systems, rely on ultra-reliable, low-latency, and deterministic communication between distributed sensors, controllers, and actuators. Ensuring such performance
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to the activities of the SIR-V2G project wich primary objective is to address the challenges arising from the large-scale integration of electric vehicles (EVs) into distribution networks by developing smart