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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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documentation. - Supporting the team in preparing and implementing various informed machine learning models for benchmarking and analysis. Desirable: - Currently enrolled in a Bachelor's or Master's program in
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scientists. We seek to appoint an expert in the research area of Machine Learning for Sustainable Processes and Materials with a focus on data-driven methods for modeling, analyzing, and optimizing complex
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The Network Analysis and Modelling uses machine learning to investigate how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. We are seeking a
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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