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Field
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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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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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reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be complemented by own lab testing e.g., SSRT incl
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to: 1. Simulate climate impacts on agro-environmental systems using a state-of-the-art biophysical process model 2.Assess adaptation opportunities for land-users under changing climate conditions 3.Assess
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://github.com/FZJ-IEK3-VSA/RESKit ). This framework currently uses historical weather data to model energy output and will be further developed to allow the simulation of renewable electricity production under
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for landowners in these regions. This project aims to: 1. Simulate climate impacts on agro-environmental systems using a state-of-the-art biophysical process model 2.Assess adaptation opportunities for land-users
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consisting of PAT and mechanistic / data driven modelling allowing process control. Steps to be taken will be: Developing a process applicable PAT method (single / multisensoric) for AAV / LNP / VLP detection
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its rich information content, conventional analysis methods have not yet fully realized its potential. This research project aims to develop a robust AI foundation model based on modern Transformer
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application from qualified women. About the position The position involves both teaching and engaging in innovative research projects on tractor autonomy, path-planning algorithms, soil compaction modeling, and
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to make it possible for the first time to link detailed cloud modeling and radiation calculations. TROPOS is looking for a PhD student (m/f/d) for the following tasks: Creation of detailed cloud simulations