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Field
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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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Computer Science, or another subject of relevance for the project. Documented knowledge and proven research experience in the area of designing algorithms and methods for data privacy and machine learning is
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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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: 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 carried out and
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chain, ranging from synthesis, cell assembly, characterization, modeling to scaled-up manufacturing. The 2-year postdoctoral project Machine Learning-based Electro-Chemo-Mechanical Estimation and Control
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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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proven experience, an area that has been strengthened by the national initiative ULF (Development, Learning, Research). Learn more here: https://www.umu.se/en/department-of-creative-studies/research
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visualization and scientific communication Extensive knowledge of relevant machine learning and AI techniques Exceptional collaborative abilities Self-motivated individual with ability to work independently
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energy-efficient and sustainable transport systems through world-class research in tribology and machine elements. Friction losses in vehicle systems still account for a significant portion of global
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, drawing on machine learning where it strengthens these methods. The research supports mission-critical scenarios and feeds into an end-to-end resilience proof of concept developed together with Swedish and