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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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Description Are you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking for a PhD
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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machine learning approaches. These are similar to earlier work on charge and excitation energy transfer (see https://constructor.university/comp_phys). The project for the PhD fellowship is slightly more
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protein structure analysis. Required skills: High motivation, curiosity, self-driven, critical thinking, strong team-player, good English, high interest in protein structure and machine learning. This PhD
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Description A PhD position is immediately available to study microbiota - B cell crosstalk. The Rollenske lab uses single B cell receptor sequencing and repertoire analysis, monoclonal antibody
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: Master’s degree in a scientific or engineering subject with excellent grades; enthusiasm for neuroscience and brain mapping; openness to new methods such as deep learning. Offer: Funded PhD position (TV-L 13
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. Correlating experimental, ab initio and multi-scale simulation as well as machine learning techniques is central to our mission: Development and application of advanced simulation techniques to explore and
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-class graduates with expertise in the CRC-addressed PhD subjects, high interdisciplinary desire to learn and willingness to cooperate, openness for internationalization and diversity, very good verbal and
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offering PhD positions for students with a background in data science, computer science, computational science, or a domain science with a strong focus on computational science and an interest in training