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Engineering, Industrial Engineering, Machine Learning, Artificial Intelligence, or related fields Expertise in numerical simulation of multi-physics systems, especially fluidic problems Expertise in generative
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power engineering. In condition monitoring non-invasive data is analyzed through machine learning algorithms or by statistical methods. The aim of predictive analysis is to use non-invasive methods
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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testing of machine learning/AI algorithms Integration of radiomic and biological datasets Working closely with Medical Physics colleagues on reviewing recommendations for detection of specific metabolites
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invites applicants for four PhD Fellowships in subsurface characterization within geosciences, reservoir engineering, molecular modelling, and machine learning at the Faculty of Science and Technology
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Intelligence (AI) and machine learning, digital sociology, AI and the sociology of knowledge, AI and expertise, and digitalization and criminology (digital criminology). A prerequisite for employment is that
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relevant PhD and medical degree alongside registration with the GMC at Specialist Registrar Garde or below. You will have a recent track record in histopathology and use of machine learning techniques, and
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PhD Research Fellowships: Artificial Intelligence Adoption, Sustainable Finance, and Twin Transition
knowledge of artificial intelligence and knowledge of natural language processing. Proficiency in statistical analysis, such as econometrics and machine learning for survey data analysis. Experience with data
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collaboration between industry, government, and academia. The Australian Institute for Machine Learning (AIML) at the University of Adelaide is the largest computer vision and machine learning research group in
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, engineering, finance, and health. Key Responsibilities: To perform the pioneer research in AI for climate transformation. To further develop data-driven and machine learning tasks for fighting climate changes