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individuals and patients. These projects involve large-scale neuroimaging data collection at 3T and 7T, computational modeling of brain responses using machine learning methods, and cross-institutional clinical
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marketing strategy, supply chain and logistics, digital distribution, and operations research. Leveraging the latest technologies, such as artificial intelligence, machine learning, and large language models
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, test and measurement methodologies for electronic modules, system engineering, data pre-processing and database indexing/analytics for dashboarding/visualisation, embedding machine learning algorithms
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publications or conference presentations (either as first-author or as a co-author) Machine learning and/or computational modelling experience Experience with brain network modelling and analysis Experience
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the area of the AI Computer Systems. The successful candidate will lead an innovative research program in cutting-edge AI computer system technologies. The position requires knowledge of both hardware and
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, or R&D environment. Experience developing CAD models and generating CAM toolpaths for CNC machining of metals and plastics. Experience performing assembly and testing of experimental equipment
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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, United States of America [map ] Subject Areas: Electrical and Computer Engineering / artificial intelligence , Artificial Intelligence and Machine Learning (AI/ML) Starting Date: 2026/01/01 Salary Range: $62,232-$80,000
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Assistant Professor in Marine Biology & Ecology - Biomedical Science or Quantitative Systems Ecology
ecologist working in coastal systems, who applies modern approaches in causal inference, experimental ecology, spatial modelling, and data science, including the use of machine learning to produce rigorous
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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