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conceptual framework linking nanoscale features to macroscopic adsorption efficiency. Generate and curate high-quality datasets to support data-driven materials optimization and future integration with AI
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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on generating new knowledge for optimizing biological conversion of carbon dioxide to acetic acid in close collaboration with an industrial end-user of the developed technology. Responsibilities and tasks Your
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platforms can unify production environments, enabling predictive maintenance and data-driven optimization through centralized data platform architectures. Your research will focus on addressing current
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during training, an effect attributed to the properties of the optimization technique. Intuitively, stochastic optimizers tend to converge to flatter minima in the complex loss landscape, which is believed
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i.e. turning towards in-line production and quality control will help save natural resources as well as reduce waste material and energy consumption. Formulation and test methods using mathematical
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cell factory development, data analysis and fermentation optimization. Oversee project planning, execution, and resource allocation. Provide teaching, scientific guidance and mentorship to PhD and Msc
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are looking for a passionate PhD candidate in Thermal Energy Systems with strong programming, optimization, and dynamic analysis of energy systems. This position is on the Horizon Europe-funded project