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broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip
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intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and
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testing and documentation). Understanding of machine learning or statistical modelling as applied to strain design is an advantage. Strong communication skills and the ability to collaborate across
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Job Description If you have solid practical experience in embedded systems, computer engineering, or related areas — and are excited to teach, collaborate, and shape the next generation of engineers
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externalities of transport. The division is interdisciplinary with scholars originating from transportation engineering, economics, psychology, computer science, social data science, machine learning, mathematics
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, bioactive compounds, and other key nutrients. Develop and apply machine learning and modeling techniques to analyse, predict, and optimize the effects of processing on food composition, food Ingredient
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tools (e.g., statistics, machine learning, optimisation, simulation) to healthcare delivery, healthcare operations and healthcare management as well as medical decision support. Your role is to build
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focused on applying quantitative tools (e.g., statistics, machine learning, optimization, simulation) to healthcare delivery, healthcare operations and healthcare management as well as medical decision
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, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted
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candidates with expertise in one or more of the following specialized areas: Machine Learning / Deep Learning Uncertainty Quantification Wind Farm Flow Modelling Wind Farm Control Wind Farm Design Wind Farm