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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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engineering, computer science or a comparable subject You have good experience in Python You have basic knowledge of the theory and methods in machine learning Good language skills in German and/or English What
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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Hybrid Crop Modelling Framework, integrating Process-Based Models (PBMs) with Machine Learning (ML) to enhance the accuracy and interpretability of crop yield forecasts, while evaluating key ecosystem
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-based AI model evaluation XAI in Physics-Informed Neural Networks (PINNs) Applications in a wide range of machine learning models, architectures, inference targets and data modalities Intersection
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) to design representations and transferable energy models for proteins and materials. Contribution to teaching on statistical physics and machine learning. The position will serve to develop your own
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computational methods using network-based analysis, machine learning and dynamic modeling. We are a young, dynamic team at the idyllic Dahlem campus and teach mainly in the Computer Science, Bioinformatics and
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programme of computer science, mathematics, physics, electrical engineering, computational linguistics, or similar with good grades PyTorch skills: experience in training machine learning models with one
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research and publications in one or more of the following areas: Item response modelling Modelling of process data (e.g., response times) for competence tests Application of machine learning methods in
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and machine learning to establish a modeling framework that uses omic data for providing effective degradation rates of biomolecules and predictions of their impact on soil organic matter turnover