54 condition-monitoring-machine-learning PhD positions at University of Groningen in Netherlands
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located across 5 European countries. MonaLisa is at the forefront of artificial molecular machine research, setting the stage for breakthroughs in chemical synthesis, nanotechnology, medical treatment and
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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described in the project overview. Owing to the current composition of the project team, there will be a mild preference for candidates opting for project 2 on “Models and machine learning”. An explanation
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spectroscopy data and AI, to automatically identify textile fabrics with high accuracy in real-world sorting conditions by (1) defining optimal spectral bands, spatial resolution, and acquisition speed; (2
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mechanics at the atomic scale. In this project, the University of Groningen will develop an array of state-of-the-art machine learning potentials for multi-component alloy systems that are relevant
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position as a hub for innovation and learning. Located in the vibrant city of Groningen in the northern Netherlands, it attracts talent from across the globe. Located within the University of Groningen
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guidelines and advice. Possesses good organisational skills and perseverance. Demonstrates competencies such as conceptual ability, presentation, planning, and monitoring progress. Our working conditions are
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to data analysis and science communication. PhD students at BirdEyes will gain expertise in cutting-edge ecological monitoring techniques, big data analysis, and creative science communication. The centre
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-edge ecological monitoring techniques, big data analysis, and creative science communication. The centre provides access to extensive global datasets, fostering collaboration across disciplines and