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Join us in designing stable materials for sustainable energy devices with machine-learning-accelerated simulation and modeling. Work assignments The postdoctoral researcher will develop machine
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PhD in Materials Science Norrkoping Reference number LiU-2026-03852 Join us in developing machine-learning accelerated simulation methods to understand and optimize interfaces in hybrid organic
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professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/ Your work assignments Machine learning, and in particular deep learning, requires
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position at the School of Science and Technology. Subject area The subject area for this position is Computer Science, with a focus on machine learning and signal processing applied to proprioceptive
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formulation, molecular-level characterization, and machine learning to develop predictive models linking chemical descriptors to adhesive properties. The work includes the development of a structured
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PhD student focusing on Machine Learning of Geometric Representations at ISY Linkoping Reference number LiU-2026-03853 We have the power of over 50,000 students and co-workers. Students who provide
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Biodiversity Variables and combining these with environmental and biodiversity data to produce pan-European species distribution and habitat maps. You will apply machine-learning methods with
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machine-learning methods with uncertainty estimation and validate the results using systematic biodiversity observation networks, spatial cross-validation, and independent national and regional
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experimentally. The research will be broadly situated in machine learning, including (but not limited to) algorithmic knowledge discovery, graph mining and social network analysis, optimization for machine
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and apply AI and machine-learning methods to infer interaction networks from acoustic co-occurrence and activity patterns, and analyse their structural properties to link topology to response diversity