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spans computational materials design, catalysis, energy materials, machine learning, and artificial intelligence. We offer a collaborative and international research environment with close interactions
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on lth.se . Subject and project description Wireless systems are becoming an increasingly important part of our everyday life. Using Machine Learning on measured wireless propagation channels as a means to
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methods that combine soundscape targets, acoustic metamaterials, physical modelling, inverse design, machine learning and perceptual evaluation. The postdoc will develop models and design methods
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machine learning for neuromorphic computing for predictive materials discovery. The doctoral student will be part of a larger project Brain-inspired AI Design of Topological Magnets for Sustainable
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-quality omics analyses and statistical and machine-learning based modeling, as well as gaining a deeper understanding in extracellular vesicle biology. Work duties and responsibilities The main task for a
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Documented ability to work in Python Experience with machine-learning methods for record linkage and text analysis Documented experience with machine-learning methods for image-to-text transcription
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understanding: detection of objects and relations between objects, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions
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. Knowledge of PyTorch and hands-on Python programming. Knowledge of machine learning and robotics, or foundation models. Awareness of diversity and equal opportunity issues, with specific focus on gender
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nutrition, such as: analysis of time series data and dynamic processes, where signals and responses evolve over time. statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and