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groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative projects with other group members and our
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to ongoing work by Dr. Megha Khosla on trustworthy graph machine learning, especially on the relationship between transparency and privacy in graph-based models. Population-scale network data are highly
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theoretically, in tight collaboration with experimental groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative
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to lanthanide molecular magnets. While the SFX2C-1e model has been recently used to model core-level spectra, its combination with EOM-CC for molecular magnets has lagged behind. We will link EOM-CC to SFX2C-1e
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to lanthanide molecular magnets. While the SFX2C-1e model has been recently used to model core-level spectra, its combination with EOM-CC for molecular magnets has lagged behind. We will link EOM-CC to SFX2C-1e
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-driven dynamical systems, perturbative theories in celestial mechanics, symbolic regression, inverse methodologies via differentiable simulations, efficient high-dimensional numerical quadrature and more
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, all of whom will be supervised by a highly experienced team of four (top) researchers in this field supported by a technician. PhD1 focuses on hybrid traffic flow modelling such as Physics inspired
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and evolution of Digital Twin Earth by undertaking collaborative research across a range of domains and advancing the use of EO for enhanced simulations, predictions and what-if scenarios. Supporting
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-of-year bonuses (8.3 %), training and career development. Our individual choices model gives you some freedom to assemble your own set of terms and conditions. Candidates from outside the Netherlands may be
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focuses on hybrid traffic flow modelling such as Physics inspired Neural Nets (PiNNs) or “ML inspired” traffic models. PhD2 focuses on data assimilation and estimating start and boundary conditions such as