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mechanics, finite element modeling, and scientific machine learning. The RSE will contribute to the design, implementation, and maintenance of open-source software libraries that integrate phenomenological
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developing innovative solutions for integrating geophysical data and machine learning approaches for geological modelling and site characterization. The position is expected to perform independent and
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 8 days ago
pipeline, building on existing deep learning models in meteorology and climate science Your Profile PhD in computational science, mathematics, physics, or a related subject Experience with machine-learning
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, where AI models are trained without having all data in a single computer. This makes it possible to use larger datasets for training, without sending sensitive data between hospitals. The goal is to
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computational mechanics and scientific machine learning. The successful candidate will work on the design of hybrid, physics-informed modeling and identification frameworks for complex dissipative material
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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robotics Goal-driven agentic AI Autonomous medical imaging Design of AI-enhanced medical devices Machine learning models and algorithms for medical signal processing Embedded AI Privacy-aware AI Foundations
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training covering topics such as computational modelling, numerical methods, statistical analysis, machine learning or data-driven analysis of complex systems Experience 0–3 years of postdoctoral experience
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modelling predictions. Experience or a strong interest in scientific programming and machine-learning-assisted data analysis for materials modelling is an advantage. PhD Position 2 – Coarse-Grained and
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engineering pipelines using Databricks Feature Store, Delta Lake, or vectorization techniques. Assist in deploying and monitoring machine learning models using MLflow or Databricks Model Serving. Implement