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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
nano-structures. In this project, we will combine numerical models, experiments, and artificial intelligence (AI) to guide the design of specific DNA nanoconstructs. The primary goal is to build an AI
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ecological benefits of large wood. Ecological Indicators 155, 111045. https://doi.org/10.1016/j.ecolind.2023.111045 Scolari, Fadul, Schwindt 2025. Hydro-morphodynamic numerical modeling indicates risk zones
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significantly slows down the development of new desirable nanostructures. In this project, we will combine numerical models, experiments, and artificial intelligence (AI) to guide the design of specific DNA
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with attractive conditions A broad range of further education and professional development programmes (for example language courses) An occupational health management model with numerous attractive
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solving. Diverse research areas: Work on numerical models, analyzing large data sets, statistical methods and more in a unique scientific environment. Who Should Apply? Emphasizing the physical system
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interest in the topics of Structural Health Monitoring (SHM), Building Information Modelling (BIM), numerical simulations and real-time monitoring systems with the use of sensor technology. Proficiency and
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Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design
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available on site for the development of suitable radiotracers. One focus of the work is on the use and evaluation of large tomographic data sets to derive parameter data for reactive transport modeling
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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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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems