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with five partners. The focus of the doctoral program is the analysis of the spatial and temporal variability of decontamination efficiency in different soil materials on a flow cell scale. The results
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into goal-directed behavior. We use state-of-the-art approaches including functional brain imaging, automated behavioral analysis, and computational neuroanatomy. We value a collaborative atmosphere, early
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description
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based at DIGIT as well as the collection and analysis of requirements from existing partners. In addition, further partners are to be integrated into the network and new formats and pilot projects
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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plan by conducting experiments, sample and data analysis, and write up of results for scientific publication are part of the PhD process – a journey to become an independent researcher! Throughout
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a four-year research project. Execution of the research plan through conducting of experiments, sample and data analysis and write-up of results for scientific publication are part of the PhD process
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of the research plan through conducting of experiments, sample and data analysis and write up of results for scientific publication are part of the PhD process – a journey to become an independent researcher
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, sample and data analysis and write-up of results for scientific publication are part of the PhD process – a journey to become an independent researcher! Throughout the project, there will be multiple
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/NL. The successful candidates commit to actively participate in networking including regular research visits to the partner laboratories. Requirements: university degree in chemistry or physics and