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resources to carry out your assignments. YOUR ASSIGNMENTS: The internship will develop and implement scalable, high‑performance algorithms for transient Lindblad dynamics tailored to the multi‑level Rydberg
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focuses on the analytical synthesis of broadband and dual-band matching networks and power combiners for 6G radar applications. The objective is to develop a component library for integration
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to collaboratively train machine learning models without sharing their data. Instead, clients exchange local model updates with a central server, which uses them to improve a global model. While this paradigm enhances
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field of innovation with strong industrial development potential. Training to strengthen your skills or acquire new ones in embedded electronics, information technology, telecommunications, and/or
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the development of the I-Segmenter [1], a fully quantized ViT framework that achieves competitive accuracy while drastically reducing the inference cost. Internship Objective The objective of this internship is to