20 msc-in-statistical-learning PhD positions at Chalmers University of Technology in Sweden
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for a PhD position that combines research in the field of intelligent mission planning and learning-based optimization with real-world applications, in collaboration with Volvo Group. This is an ideal
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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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student at Chalmers, you are an employee and enjoy all employee benefits. The position is limited to four (4) years, with the possibility to teach up to 10%, which extends the position to 4.5 years
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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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, you must have a MSc degree corresponding to at least 240 higher education credits by an internationally recognized university in materials science, mechanical engineering, chemical engineering, physics
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interfaces and driver modelling Implementation of control algorithms in mechatronic systems Experimental design and statistical methods Vehicle testing and test methods involving human test subjects What you
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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