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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 components are in use
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machine learning techniques to develop local graph representation models, which will be aggregated globally to enhance their predictive power and translational relevance, all while maintaining strict data
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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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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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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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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
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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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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