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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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theories and numerical methods, carrying out and analysing field and remote sensing observations and conducting and analysing numerical model simulations. The PhD position is funded by the German Research
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methods Knowledge of handling radioactive materials is considered an asset Experience in radiochemical methods is considered advantageous Highly qualified and highly motivated graduates and interested in
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) and the University of California Irvine (UCI). The Research School "Foundations of AI" focuses on advancing AI methods, including energy-efficient and privacy-aware algorithms, fair and explainable
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of new EEG and MEG neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be
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and the effects of disordered correlated microstructures on diffusion; iii) development of energy-based models and numerical simulations of hyperuniform assemblies; iv) development and application
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structured PhD curriculum. These meetings will include presentations by faculty members on various research topics and methods, offering students valuable insights into different approaches and techniques
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breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
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particular, geomagnetism) and the development of corresponding numerical methods. We offer the opportunity to work in a small interdisciplinary research group consisting of mathematicians, computer (geo
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skills and experience with numerical modeling and particle-based methods Interest in working closely with experimentalists Excellent written and spoken English skills Experience with parallel programming