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position for candidates interested in interpretable AI, stochastic optimal control, deep learning and high-impact research in sustainable mobility. About us The position is located at the Systems and Control
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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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and development Generative AI models for sound, music, visuals, 3D graphics, or movement Projects related to Generative AI Background in mathematics and statistics of Deep Learning What you will do
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Join the cutting-edge RAM³ project: Unlocking the Potential of Recycled Aluminium through Machine Learning, High-Throughput Microanalysis, and Computational Mechanics. We are offering a PhD position
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and/or practical experience with measurement techniques in fluid dynamics and heat transfer. Contract terms The position is limited to four years, with the possibility to teach up to 20%, which extends
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable
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Aluminium through Machine Learning, High-Throughput Microanalysis, and Computational Mechanics” - a multidisciplinary research effort at the intersection of machine learning and materials science. This
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the Swedish National Infrastructure for Computing (SNIC) and the Chalmers Centre for Computational Science and Engineering (C3SE). Learn more about the project and the research: Project overview Due
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15 full-time researchers offers a stimulating and supportive environment to learn and grow. Your profile Required qualifications: Undergraduate degree in Engineering, Physics or Mathematics with strong
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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