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Field
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engineering, machine learning, molecular design, and sustainability, helping to create smarter ways of identifying promising sorbents for electrochemical CO2 capture. Over the course of the project
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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within
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, and machine learning methods. Using regression analysis and vector autoregression (VAR) models, the study examines the relationship between macroeconomic variables and the performance of various
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and mapping, light fields, extended reality (XR) technologies, sim-to-real, synthetic data generation, and advanced computer vision and machine learning techniques. In addition, the group works on
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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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diffraction data where the information extends towards 3-d space. Machine learning offers promising approaches for the solution of complex problems of disorder, ultimately aiming at general and automated
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training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical
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conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in-distributed-wireless-systems/ Distributed MIMO
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spoken and written is required. The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative- genomics/ ) at Institut Pasteur, led by Laura
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://cavecore.eu/ Your Research Project (DC4) You will work at the intersection of machine learning, control theory, and autonomous multi-agent systems to develop hybrid learning-based control strategies