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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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complex contexts through statistical models, machine learning (ML) methods, and artificial intelligence (AI). This includes working with performance, scalability, resilience regarding platform architectures
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, calorimetry, and synchrotron experimental measurement techniques. Knowledge of AI-based and machine-learning methods is also beneficial. For further information about a specific subject see General syllabus
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, micro-CT, particle size analysis, calorimetry, and synchrotron experimental measurement techniques. Knowledge of AI-based and machine-learning methods is also beneficial. For further information about a
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with European industry. The EISLAB division at Luleå University of Technology conducts research in electronic systems design, sensor systems, cyber-physical systems, the Internet of Things and machine learning
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. The EISLAB division at Luleå University of Technology conducts research in electronic systems design, sensor systems, cyber-physical systems, the Internet of Things and machine learning, and works on
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nutrition, such as: analysis of time series data and dynamic processes, where signals and responses evolve over time. statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and
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in English (reading, writing, speaking). • Show ability to work independently as well as in a team. • Good knowledge in AI, machine learning, data science and mathematics. • Good knowledge in one
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for screening the subsurface to select areas with H2 -potential source rocks; - acquire and process new MT and controlled-source electromagnetic (CSEM) field data in targeted areas; - perform integrated 3D
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PhD Position in Engineering Materials Project: Development and Characterization of Advanced Sorbents
machine elements. In the undergraduate program, the main two programs focusing on the international profile of the Division of Materials Science are important for the department, namely the EEIGM master's