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systems or smart buildings, such as regression, classification, time series analysis, or basic predictive modelling. Experience with data handling, including data cleaning, transformation, exploratory
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potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network potentials (ENNPs) to model nanoparticle energy surfaces. Building atom
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structures obtained via Dark-Field X-ray Microscopy (a synchrotron-based imaging technique), combined with phase field modeling predictions of the structural evolution. Qualifications: Background in data
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potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network potentials (ENNPs) to model nanoparticle energy surfaces. Building atom
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design and laboratory experimentation, you will first explore a broad range of sodium-oxide glass compositions using advanced computer models to predict how well ions can move through them. Based
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interfaces This project explores advanced inverse design methods for next-generation EDC photonic systems. By combining topology optimization with physics-based modeling, it aims to discover new light–matter
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, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12 PhD candidates will be based across seven different universities in Europe: four in
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how these factors influence performance, safety, and lifetime predictions. Your research will involve constructing probabilistic and statistical models—such as Bayesian inference, stochastic simulation
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developing the competences of the IML group within prediction of T and B cell immunogenicity and immunoinformatics in general. You will work with nearby bioinformatics, postdocs and PhD students working
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will be centered on further developing the competences of the IML group within prediction of T and B cell immunogenicity and immunoinformatics in general. You will work with nearby bioinformatics