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round Details This project aims to develop a non-invasive brain-machine interface (BMI) that allows a user to direct a semi-autonomous robot to perform different tasks through brain signals
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out annually in the UK to relieve pain and restore mobility. Despite its success, some patients continue to face complications such as discomfort, instability, or early implant failure. A key factor is
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metal work. Lays out and fabricates of duct lines, drip pans, metal louvers, etc. from flat sheet metal. Fabricates and installs items made of special metals such as ornaments, murals and cornice work
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are seeking two motivated researchers or engineers with expertise in finite element programming to join an ambitious project modelling the complete neuromuscular dynamics of Drosophila larvae. This position
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) , Lattice QCD , Machine Learning , Neutrino Astronomy , Neutrino physics , Nuclear and Many-Body Theory , Nuclear Theory (nucl-th) , Particle Astrophysics , Quantum Field Theory Appl Deadline: 2026/01/02 04
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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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machine learning algorithms for the prediction of manufacturing processes in composite materials. Development of user subroutines for finite element constitutive models Validation of model and numerical
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"System architecture and user requirements" - WP3: "RAISE Suite Machine Actionable DMP, SDK and Central Hub", D3.1 "RAISE Suite SDK (interim version)" - WP6: "D6.1 Dissemination, Exploitation
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Office of Faculty of Computer and Mathematical Sciences Founding Dean of Faculty of Computer and Mathematical Sciences (Ref. 241030001-IE) This new Faculty of Computer and Mathematical Sciences
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-computer interaction (UX), and/or appli cation of Machine Learning. • Sense of responsibility and ability to communicate and integrate into multidisciplinary work teams. Financial component