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. Conduct transportation resilience assessment and enhancement studies based on GIS, complex network analysis, and machine learning. Simulate human mobility in response to extreme weather events (e.g
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spectral based sensing, including Ultrasound and Hyperspectral Imaging (HSI), Artificial Intelligence (AI) and Tiny Machine Learning (TinyML). Duties As a Postdoctoral researcher you are expected to perform
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as natural experiments and interpretable machine-/deep-learning models. Publish research results in high-quality international journals (at least two peer-reviewed papers are expected). Eligibility
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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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Do you want to contribute to the future of AI-driven electric transport systems? Join our research group to develop advanced machine learning methods for electromobility, focusing on energy-aware
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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science. Our research spans computational materials design, catalysis, energy materials, machine learning, and artificial intelligence. We offer a collaborative and international research environment with
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, ROS) Solid skills in numerical analysis Advanced knowledge of computer vision Experience in human–robot interaction Particularly Meritorious It is particularly meritorious if the applicant has: A PhD
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, ROS) Solid skills in numerical analysis Advanced knowledge of computer vision Experience in human–robot interaction Particularly Meritorious It is particularly meritorious if the applicant has: A PhD
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of AI-driven electric transport systems? Join our research group to develop advanced machine learning methods for electromobility, focusing on energy-aware coordination of electric vehicle fleets