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. Furthermore, we use the olfactory network as a model to study the dynamics of neuronal development, synaptogenesis, neuronal degeneration, and regeneration. Our research is complemented by behavioral studies
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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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to adapt to climate change. It builds up a national and international network structure in order to integrate existing competences and knowledge, and to link various actors within the complex area of climate
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the dynamics for increasing system complexities implement and optimize the relevant quantum circuits on atomic and superconducting processors cooperate and actively work with experimental partners developing
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to investigate the influence of glucosinolate-based amines on the formation of Maillard-like products in model systems and foods, to isolate and identify the resulting products and to find out whether
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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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working atmosphere flexible and family-friendly working time model and the possibility of mobile working (up to 50% of working time) subsidy for a company ticket for public transport
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to facilitate a rapid and efficient exchange among experimental and computational groups and Devise an approach in invertible predictive modelling that links semiconductor properties to the composition of lead
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working atmosphere flexible and family-friendly working time model and the possibility of mobile working (up to 50% of working time) subsidy for a company ticket for public transport
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an exceptional international team with expertise in all aspects of the project. Your tasks will include: • Preparation of different EO and in-situ datasets for training a machine learning model • Development of ML