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Field
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science, with a particular focus on neuroscience applications. Designs AI techniques and algorithms for multimodal data fusion (e.g., MRI, EEG, cognitive and behavioral data, blood biomarkers, and
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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systems on software defined radio (SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide
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) and common data formats (e.g. RVT and DWG). Preference will be given to those with experience in computer graphics, 3D geometric algorithms or WebGL development. Applicants are invited to contact Prof
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John Williamson, and Dr Sebastian Stein. The job requires the proven ability to develop novel theory and build and evaluate working interactive prototypes involving complex computational models
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(SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide enhanced interference resilience
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work alongside renowned academics and researchers in ENU’s School of Computing, Engineering and the Built Environment. If you are someone with expertise in multimodal speech processing and AI algorithms
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with major in Power Engineering • Relevant experience in power electronics, drives, control algorithms, optimization techniques, and optimal energy management algorithm development for the optimal
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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computer programming to verify the efficiency of the designed solution algorithms Analyze data acquired from the field survey Develop machine learning models for prediction and recommendation Job