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English. Preference will be given to those with (i) strong background in quantitative methods, geospatial methods, AI and machine learning; (ii) experience in high-performance and cloud computing; (iii
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requirements and focusing on data-value maximisation. This project will utilise innovative machine learning methods and tools from process systems engineering to simultaneously optimise product quality and the
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advanced data science methods, including statistical modeling and machine learning approaches Developing and applying novel causal inference methods, such as target trial emulation and causal AI frameworks
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degree in AI, Computing Science, Mathematics, or Data Science. Strong coding, communication and organizational skills. Demonstrable experience with using machine learning packages (e.g., PyTorch
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/physics or any related discipline. This is a largely experimental research project based at the University of Nottingham, with some aspects of material modelling and development of machine learning to aid
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-physics modelling of power electronic systems and components, with special focus of magnetic components, Incorporating physics-driven machine learning approaches in power electronics design, Incorporating
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The University of Luxembourg invites applications for a fully funded Ph.D. position in machine-learning force fields (MLFFs), uncertainty quantification, and atomistic simulations within the FNR
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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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armasuisse Science and Technology and partners from industry and the Swiss Armed Forces. The research contributes to the scientific foundation of monitoring and rapid altering systems for underground
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on epithelial barrier integrity, inflammation, and host transcriptional responses. The project offers interdisciplinary training in bioinformatics, advanced statistics and machine learning, anaerobic microbiology