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Field
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites Research Groups at the Faculty of Engineering, which conduct cutting-edge research
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implementing signal processing algorithms specifically tailored to analyze signals that contain interfering impulsive content, often encountered in data coming from main and pitch bearings. Machine learning
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considered. Qualifications/Skills PhD degree in a programme relevant to human-computer interaction and/or critical computing, ideally in Computer Science, Industrial Engineering, Interaction Design, or a
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, machine learning and data analytics, multi-objective optimization, life cycle assessment (LCA), serious gaming, and other participatory research methodologies. Candidates should also demonstrate leadership
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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are looking for a highly motivated and skilled PhD researcher to work on structural surrogates of offshore wind foundations through graph-based machine learning. Our goal is to perform full-structure
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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the first call lasts from the 1st of July to 31st of August 2025. Description of specific PhD projects: Machine Learning Interatomic Potentials for Chemical Reactions Hosting: Tallinn University of Technology
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Engineering Operations Research Appl Deadline: (posted 2025/06/24, listed until 2026/06/23) Position Description: Apply Position Description Overview Susquehanna is expanding the Machine Learning group and