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of computation, and thus continuous aspects, into rule-based models of graph transformation in order to combine the individual strengths of both paradigms. Rule-based models are transparent and explainable
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thus continuous aspects, into rule-based models of graph transformation in order to combine the individual strengths of both paradigms. Rule-based models are transparent and explainable; they make sense
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the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics
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level in computer science or completed courses with a minimum of 240 credits, at least 60 of which must be advanced courses in computer science, mathematics, AI, machine learning or similar. Alternatively
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thermodynamics, energy technology, or systems modelling. Solid knowledge in mathematics, physics, thermodynamics, energy technology, optimization, and programming for system modelling, with experience in tools
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system modelling. Solid knowledge in mathematics, physics, thermodynamics, energy technology, optimization, and programming for system modelling, with experience in tools such as Matlab, or Python
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qualifications You have graduated at Master’s level in computer science or completed courses with a minimum of 240 credits, at least 60 of which must be advanced courses in computer science, mathematics, AI
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areas, of which at least 30 credits must be at an advanced level. Courses in statistical analysis, quantitative methods, or mathematical models acquired outside these subject areas may also be included
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areas, of which at least 30 credits must be at an advanced level. Courses in statistical analysis, quantitative methods, or mathematical models acquired outside these subject areas may also be included
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this, we focus on self-supervised denoising, where models learn to restore images using only the noisy data itself — without requiring clean references. Existing approaches often rely on convolutional neural