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Field
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uses machine learning to improve online accelerator models and that develops correction algorithms for accelerator operations. This position is for a 2-year research project. This position has a high
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, Massachusetts 02215, United States of America [map ] Subject Areas: Computer Science / Big Data , Data Science , Algorithms , Artificial Intelligence , Databases , Machine Learning , Security and Privacy
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integrating IoT sensor data, ML algorithms, and energy system modelling / simulation. Develop engineering-based simulations to understand operational impacts on energy output and maintenance needs. Prepare
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at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will involve designing innovative QML approaches and collaborating
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Digital twin railway network School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof D Fletcher, Prof R Harrison Application Deadline: Applications accepted all
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data and developing the algorithms and tools that enable the work of their team. Thus, the ideal candidate for this position must have a biological background, the ability to translate scientific
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procedures (e.g., multilevel modeling, longitudinal data analysis, machine learning algorithms), cleaning and structuring large datasets, validating model assumptions, and ensuring reproducibility. Synthesizes
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the best performance from the CCD during the SMILE mission lifetime. Support the analysis of the SMILE CCD radiation damage studies and support development of radiation correction algorithms for the SMILE
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division at the Department of Electrical Engineering (ISY), a vibrant research environment with a total of 38 employees, including 9 researchers, 18 PhD students, and several adjunct researchers. CVL
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collaboration with other researchers. Your profile • Formal requirement: Doctoral degree (PhD) in quantitative genetics, tree breeding, biometrics, statistical genetics, bioinformatics or related field