11 condition-monitoring-machine-learning PhD positions at Chalmers University of Technology
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of robotics, electromobility and autonomous driving. We offer advanced PhD courses where we extend the fundamentals in optimal control, machine learning, probability theory and similar. The research and
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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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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for Quantum Technology (WACQT, http://wacqt.se ). The core project of the centre is to build a quantum computer based on superconducting circuits. You will be part of the Quantum Computing group in the Quantum
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marine monitoring data Author scientific articles and other scholarly publications Present research findings at conferences and to relevant stakeholders Collaborate with researchers, industry professionals
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or conducting water sampling) Collecting and analysing empirical and monitoring data Writing scientific articles and other publications Presenting your work at conferences and to stakeholders Collaborating with
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characterisation to machining of primarily metals. We employ a range of technologies - powder metallurgy, electroplating, additive manufacturing and material removal - and a range of advanced characterisation
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team of over 15 full-time researchers offers a stimulating and supportive environment to learn and grow. Your profile Required qualifications: Undergraduate degree in Civil Engineering or a related topic
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courses, including several master’s programmes. Learn more at: www.chalmers.se/en/departments/e2 Qualifications To qualify, you must: Hold a Master’s degree (or equivalent, 240 ECTS) in Engineering Physics
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reinforcement learning, robotics, and the development of reactive software systems. It enables the creation of robust, reliable programs by specifying what a system should do, while automatically deriving how it