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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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latest predictive and generative AI for materials, we can offer you the best possible foundation. We seek two highly motivated and talented PhD students to join our group at DTU Compute, and we offer
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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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and predict cyber threats. You will work closely with international collaborators, including Eindhoven University of Technology (TU/e), and with industrial partners, providing realistic case studies
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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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control, open-source background checks may be conducted on qualified candidates for the position. The Research Group for Genomic Epidemiology conducts targeted research with the aim of predicting and