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Field
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model
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tailored surveys for enriching the understanding of the mixed system. The behavioural understanding on the demand side will provide information to the supply side, such as the trade-offs between different
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are differentially private algorithms for statistical model parameter estimation under different trust relations. About the project The position is funded by the Norwegian Research Center for AI Innovation and will be
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model
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provide information to the supply side, such as the trade-offs between different attributes of the system, or the acceptable walking and waiting times, which will be used both to adapt the design and to
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, and Máxima Medisch Centrum focused on the development and implementation of analytical assays and decision support algorithms in clinical practice. Additionally, the project involves collaborations with
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, regulating different types of funding for the promotion of scientific research, technological development and innovation at the University of Alicante (Official Journal of the University of Alicante of 22
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reliable and reproducible measurements across different assays. In this PhD project, you will develop RMPs and reference materials (RMs) for several protein TMs to enable harmonized and reproducible
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opportunity to tackle these two complementary perspectives. In the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools
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. Expected outcomes include: development of novel algorithms that significantly improve predictive accuracy for equipment failure; creation of scalable monitoring systems that reduce operational costs