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for optimizing metals microstructures in-situ during the AM process as well as ex-situ during post-AM treatments and enable predictions of the microstructural evolution, and thus changes in properties, while AM
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functions of single nanoparticles as well of ensembles with varying number of nanoparticles. Advancing the understanding of corporative interactions in nanoparticle catalysis, including ensemble-averaging
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inhibitor candidates with high predicted affinity and selectivity. These designs will then be experimentally validated through a combination of affinity binding assays, enzymatic activity measurements
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are expected to be significant in: Earth system science – by improving models of Earth surface evolution and enabling better predictions of landscape response to climate change. Engineering and applied physics
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and operation of building HVAC systems, these technologies support both energy efficiency and flexible demand objectives. Model predictive control (MPC), which involves physics-based building energy
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. Responsibilities and qualifications You will contribute to the development of a computational framework designed to predict the degradation mechanisms of organic electrolytes. The framework will rely
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to apply machine learning techniques to a combination of experimental data and simulation results, aiming for faster and more accurate predictions. About us You will join an international and highly
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production environments, enabling predictive maintenance and data-driven optimization through centralized data platform architectures. Your research will focus on addressing current bottlenecks in data and