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for validation of CFD results. Implement novel unsteady CFD and next generation in-house AI based design tools validated by the gathered experimental data on GKN resources with tight collaboration
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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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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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many research synergies coming together on the main thread of machine learning and Artificial Intelligence (AI). The successful candidate will join the newly established research group AI in
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control and its integration with learning-based motion prediction under uncertainty. - Validate methods through simulation and collaboration with industrial partners (Volvo Cars and Volvo Group). - Publish
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at the Division of Fluid Dynamics, within the Department of Mechanics and Maritime Sciences at Chalmers. The project is carried out in collaboration with Vattenfall Research and Development, and is part of
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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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the right one for you! This is a fully funded PhD position to develop micromechanical models of high-pressure die-cast aluminium, a unique opportunity for a motivated individual to work in a collaborative
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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
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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