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Field
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. Previous experience with machine learning applications in molecular modelling, including experience with at least three of the following Python libraries: TensorFlow, PyTorch, JAX, RDKit. Previous
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for Communication Systems carries out research, undergraduate and postgraduate education in communications engineering, statistical signal processing, network science, and decentralized machine learning. Welcome
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broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling
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-state model will be approximated using machine-learning surrogates and will be used for a real-time optimization, such that the plant operates optimally despite disturbances. The candidate will be part of
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, medical informatics, databases, data mining, machine learning, applied mathematics, biomedical modelling and analysis of complex networks. Joint data science projects between the different partners
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combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures
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to machine learning. This PhD provides a unique opportunity to shape emerging concepts in Artificial Intelligence Informed Mechanics (AIIM), combining fundamental research with methodological innovation. You
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willing to work in a collaborative environment. Preference will be given to those with (i) strong background in quantitative methods, geospatial methods, AI and machine learning; (ii) experience in high
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design next-generation computer architectures for running large AI models on embedded and edge systems under strict timing, energy, and memory constraints. You’ll explore hardware-aware optimization and co
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quantitative focus on these fields Solid foundation in statistics and/or machine learning, e.g., supervised learning, regression modeling, model evaluation, or high-dimensional data analysis Good programming