36 condition-monitoring-machine-learning PhD positions at Chalmers University of Technology in Sweden
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develop innovative remote sensing capabilities to monitor oceans, ice, vegetation, and natural disasters. Be part of a dynamic, international team shaping the future of environmental monitoring! About us At
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at the same time so special. The originality of the experiments is in the combination of X-ray based scattering and imaging methods to monitor the changes at the particle scale during testing. Research
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the Division for Computer network and systems and the employment is placed with Chalmers University of Technology. Our research spans from theoretical computer science to applied systems development. We provide
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sustainable design, product development and environmental assessment will conduct case studies to integrate user behaviour into the early design of dishwashers, washing machines, refrigerators and ovens
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. The position is placed in the Division for Computer Networks and Systems and is formally employed by Chalmers University of Technology. Our research spans from theoretical computer science to applied systems
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livable cities. The project will be based in the AI Laboratory for Molecular Engineering (AIME) , led by Assistant Professor Rocío Mercado Oropeza, where researchers develop new machine learning (ML
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students pursue their Ph.D. in a similar area, which plenty of opportunity to collaborate and learn from and with peers. About the research project This ad is for a Ph.D. student researcher that will work in
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the research project This project is set to explore so-called shared control between the driver of a car and the car's safety systems. By mechanically disconnecting the driver's steering wheel from
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occupational traffic safety, with a focus on the car driving behavior of employees who drive as part of their job but are not professional drivers. Lack of knowledge, training, stress, fatigue, and poorly
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for the construction of runtime monitors that capture under what conditions an ACPS is guaranteed to maintain safety. A key challenge in developing such monitors is to handle noisy, missing, or delayed data