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-based transfer learning classification model for two-class motor imagery brain-computer interface. International Journal of Neural Systems (IJNS). https://doi.org/10.1142/S0129065719500254 * Kudithipudi
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's doctoral program (https://www.ntnu.edu/nv/phd) PLEASE NOTE: For detailed information
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representatives o carrying out data analysis and report writing. Candidates will have (or be working towards) a postgraduate degree (ideally PhD) in any of the sectors: Data Science / AI Machine Learning
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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information from clinical notes ? Implementing machine learning models for prediction and classification tasks in cardiovascular populations ? Cleaning, preparing, and managing large healthcare datasets
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both independently and as part of an international, interdisciplinary team Assets•Experience with computer vision or deep learning (e.g. PyTorch, TensorFlow)•Familiarity with street view imagery or other
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focus on the dynamic nature of phase transitions in APIs, using machine learning interatomic potentials (MLIPs) to construct force fields whose mathematical complexity will be carefully controlled in
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developments in sensor design, dataset transmission, data analysis, and numerical modeling to distinguish between normal and abnormal features. Here, the goal is to develop machine learning algorithms
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at a computer for large portions of the day; repetitive motion; occasionally positioning patients over 25 lbs. Shift Monday – Friday, Day Shift; 7:30-6:00pm (40 hrs/wk), 4x 10-hour shifts Job Summary We
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning