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developing synthetic datasets using simulation environments. Familiarity with the theory and implementation of diffusion models and GANNs applied to the same context. Minimum requirements: • Knowledge
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the thesis with your application. Documentation of your completed PhD degree must be submitted before commencement. Demonstrated knowledge of pedagogical theory, particularly linked to approaches such as
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research interests encompass a broad range of topics, including discrete mathematics, finite model theory, and the complexity of logical systems, as well as the foundations of AI, explainability, and answer
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theory & experiment: Co‑design validation experiments with experimentalists; iterate models using feedback from new measurements. Automate the workflow: Build Python workflows for simulation and data
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& advance digital twins: Integrate electronic structure (e.g., DFT, ab initio MD, tight-binding) with multiscale simulations to predict experimental observables at interfaces. Bridge theory & experiment: Co
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the master's degree has been awarded. A strong interest in Algorithmic Number Theory and/or Cryptography is a requirement. Experience in programming is an advantage. Experience in developing and coding
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, from full complexity climate models, to turbulence models, to in situ data. The work will be at the interface between data analysis and theory. We are also open to employing data-driven techniques
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measurement quality issues related to respondent non-compliance in ecological momentary assessment or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models
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. The work will be at the interface between data analysis and theory. We are also open to employing data-driven techniques if the candidate wishes. The ocean is currently evolving with the changing climate
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. Documentation of your completed PhD degree must be submitted before commencement. Demonstrated knowledge of pedagogical theory, particularly linked to approaches such as project-based learning, problem-based