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Field
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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such as the NEPS. Potential research areas include (but are not limited to): Item response modeling of achievement tests Analysis of process data (e.g., response times) to enhance competence measurements
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Erlangen-Nürnberg). Course of study The IDK Practices of pre-modern Cultures, Global Perspectives and Future Concepts programme strives to offer a best-practice model of doctoral education. This includes a
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of biogeochemical processes with an emphasis on terrestrial ecosystems Development of observational techniques to monitor and assess biogeochemical feedbacks in the Earth system Theory and model development
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empirically investigate and linguistically model the effects of uncertain information on the common ground. In particular, it studies the role and effect of epistemic and evidential marking and seeks to explore
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will lie on developing machine learning models for regression and reinforcement tasks to work with, enhance or replace established methods from computational engineering and computer simulation (such as
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: Prof. Dr. Steven Travis Waller, Chair of Transport modeling and simulation, and co-supervised by at least one additional professor, plus an international tutor of the CRC Requirements: excellent
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susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be
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analysed with bioinformatics tools providing the basis for systems-biological models for an understanding of interactive networks in multi-partner and multi-level consortia. Continuous exchange of knowledge
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with high-dimensional, often noisy, data sets; and mathematical modelling approaches that reduce the dimensionality of parameter spaces and produce mechanistically realistic, experimentally testable