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decision-making, computational efficiency, generalization under changing market conditions, and safe constraint handling. The PhD candidate will develop and validate decision-support methods based on deep
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resources efficiently. In this PhD project, you will develop mathematical theory and computational methods for the analysis and design of chaotic sampling mechanisms in networked control systems. You will
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such as mechanistic, chemometric, deep learning, and physics-aware models. Improve robustness and reliability of the developed methods for deploying AI models in real environments utilizing augmentation
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of cryptographic implementations and hardware-security countermeasures. Experience with hardware reverse engineering, debugging interfaces, or firmware analysis. Experience with machine learning, deep learning
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in one or more of the following research areas is desirable: geometric numerical integration, structure preserving deep learning, stochastic differential equations, generative AI, numerical
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implementations and hardware-security countermeasures. Experience with hardware reverse engineering, debugging interfaces, or firmware analysis. Experience with machine learning, deep learning, signal processing
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, structure preserving deep learning, stochastic differential equations, generative AI, numerical optimization. Strong programming skills (Python, Julia, Jax). Experience with numerical optimization is also
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, Mathematics, Engineering or a related discipline, with an interest in AI applied to complex and safety-critical systems. Good knowledge of Machine Learning and Deep Learning methods, including experience with
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an unnecessary discarding of generated power. This four year’s PhD position is aimed at using captured CO2 from point sources and hydrogen from water electrolysis to generate carbon-based base chemicals and fuels
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and economic use of renewable electricity without an unnecessary discarding of generated power. This four year’s PhD position is aimed at developing multiphase models to predict the dynamics and