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the field of Machine Elements. The main aim of this PhD student position is to strengthen the newly started research on Triboelectrictive nanogenerator (TENG)-based smart lubrication in Machine Elements
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on information theory, machine learning, and control to analyse how local variability, sensor drift, and platform differences affect both global model performance and human supervisory factors such as workload and
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for using AI to develop social engineering attempts. This project combines human subject research of learning and decision making, Human-Computer Interaction, and the advancement in AI methods
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with a strong background in mathematics, computer science, or machine learning. The work has a strong focus on developing new objectives or new architectures for medical deep learning and on new ways
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longitudinal within-person models, discrete-time survival analysis, and/or explainable machine learning. Each of the three work packages is designed to result in one publication, together constituting a
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electric machines for future defence and aerospace propulsion systems. The research will explore innovative machine topologies and advanced multi-physics design methodologies to enable electric machines with
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check the minimum entry requirements for the PhD . Applicants must also satisfy Monash’s English Language Proficiency requirements; Demonstrate knowledge of machine learning, medical image analysis
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systems and control, applied mathematics, engineering, or a related field A strong background or interest in systems and control, applied mathematics, machine learning, and affinity with biological systems
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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12.01.2026, Academic staff The Professorship of Machine Learning at the Department of Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%; initial contract 1.5