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-learning models on resource-constrained hardware Candidates should also have experience or strong interest in Edge AI, TinyML, embedded machine learning, neuromorphic computing, or signal processing . We are
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waves with high precision and in confined volumes The position will be embedded within the research group of Dr. Kai Melde (IMSEAM, Heidelberg University) and co-supervised by Prof. Carsten Rockstuhl
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