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29.04.2021, Wissenschaftliches Personal This PhD position is in the field of resource management for wireless network that leverage “digital twins” modeling aspects of the physical (such as user
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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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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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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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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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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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The Institute of Coastal Ocean Dynamics at the Helmholtz-Zentrum Hereon is investigating the observation and quantification of ocean mixing, and its representation in ocean models. We therefore
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are an advantage: femtosecond laser and diagnostics, high power lasers, ultrahigh vacuum, programming skills (Labview, Python) Ability to work closely within a team: engineers, students, postdocs and scientists, and
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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