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Are you interested in developing computational tools to understand the detailed mechanical behaviour of multi-phase materials? Then this PhD position at Chalmers University of Technology might be
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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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Master’s degree in Applied Mechanics, Mechanical Engineering, or a closely related field. Strong knowledge of fluid mechanics, CFD, turbulence modelling, and structural mechanics. Understanding
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our research team for addressing a timely societally relevant problem. Project overview The aim is to unravel the mechanisms, and time scales involved at particle scale, for the formation and failure
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Analysing biofilm structure and microbial communities Additionally, you will develop a mathematical model that includes both adsorption and biodegradation mechanisms. This model will be calibrated using pilot
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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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, 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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fields: Robotics Computer Science Electrical and Computer Engineering Mechanical Engineering Applied Mathematics Applied Physics Statistics and Optimization A strong background in robotics, machine
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at the Division of Transport, Energy and Environment at Chalmers University of Technology, Department of Mechanics and Maritime Sciences . You will join an interdisciplinary team with Ida-Maja Hassellöv, Lars
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We are looking for a highly motivated PhD student to join the research group of Professor Christian Müller . The focus of the project is on understanding the mechanical properties of conjugated