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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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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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modelling, and prediction tools. Fouling Control Coatings Fouling Control is performed by specifically designed materials to remove or prevent biofouling from i.e. ship hulls, as bio fouling leads
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modifications using high-resolution mass spectrometry and AI-based de novo peptide sequencing. Develop and apply machine learning models to predict protease activity and substrate specificity, integrating protein
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safely share data while maximizing utility–privacy trade-offs. Decision-support pipeline: fuse predictive and prescriptive analytics, so that forecast providers and aggregators can maximize the value
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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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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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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