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theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise
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usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and algorithms for resource-efficient
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isolation algorithms and data-driven classifiers. As postdoc, you will principally carry out research. You are expected to actively publish and present results in scientific journals and conferences. A
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performance should improve over time as more data becomes available. The diagnostic conclusions will be presented to an operator using a combination of AI-based fault isolation algorithms and data-driven
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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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., microfluidic channel optimization, polarization-dependent scattering studies, spectral imaging implementation, or algorithm development). Planning experimental campaigns, simulations, and modeling efforts
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algorithms. Our research integrates expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems
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algorithmic aspects related to the development of highly accurate, efficient, and robust AI models capable of operating effectively within complex and dynamic radiofrequency spectral landscapes, accounting
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of approaching reconstruction and variability analysis. The project combines applied mathematics, computational imaging, and structural biology. You will develop algorithms, implement and test software tools, and
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We are seeking a postdoc to co-design efficient and realistic simulation algorithms for noisy quantum circuits in superconducting hardware, combining quantum modeling with hardware-aware performance