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Field
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on developing hybrid traffic flow models that combine physical modelling principles with machine learning approaches, such as Physics-Informed Neural Networks (PINNs) and machine-learning-enhanced traffic models
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, computer vision, federated learning, foundation models, adaptation techniques, multimodal learning, longitudinal image analysis or related areas, evidenced through coursework, research projects
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. Experience with finite element software development. Experience with machine learning and data-driven modelling. Experience with high-performance computing. Previous scientific publications. Qualification
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learning, computer vision, or a related field; knowledge of affective computing, generative AI models, and deep-learning methods; proficiency in Python and experience with machine-learning libraries
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, or supervised/unsupervised learning depending on the available data) using spatial analysis and geographic machine learning tools (e.g., scikit-learn, PyTorch/TF + GeoPandas/Shapely) - Implementing a semantic
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theory, physics-based simulation, and machine learning. Job description The PhD project will develop machine-learning methods for atomistic materials modeling Possible research directions include machine
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methodological developments along the chosen direction (inference, active-matter theory, or machine learning). ◦ Algorithmic implementation and validation of the developed tools. 5. Validation on model systems
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of high-performance computer systems You preferably have experience with hydrology, and affinity or experience with agent-based modelling and large-scale data processing with societal relevance You are able
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. This research emphasizes improving prediction accuracy for renewable energy production (particularly solar and wind) through advanced machine learning techniques that capture spatial-temporal dependencies
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and experience with modern deep learning frameworks (e.g. PyTorch) Solid background in machine learning, ideally with experience in NLP, large language models, or sequence modeling Interest in clinical