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. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ Background and description of tasks Our group develops new single-cell multiomic methods to characterize microbial
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-on artificial intelligence. To achieve the vision, research will focus on improving the hardware for both 6G devices and space-based satellites, developing signal processing methods that handle large signal
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in example design, development or operations of small satellite hardware or software. Applicants should also have good communication skills in written and oral communications in English language
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distributed wireless systems" which is conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in
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(e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as
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continues to surge, the steady upscaling of quantum hardware (now reaching thousands of qubits) has resulted in increasingly sophisticated quantum software pipelines. Given the strategic use of quantum
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inference and deployment costs (e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects