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Field
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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learnable physical kernels, geometry encodings, and boundary-aware layers; compare to PINNs, U-Nets, graph operators, and transformer baselines. Learning strategy: physics-informed and multi-task losses
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be developing advanced spatial models such as graph-based approaches and network analytics to predict how blue network dynamics, fragmentation and surrounding land use interact to shape ecosystem
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areas, including generative modelling (e.g. diffusion models, flow matching, self-supervised and autoregressive approaches), causal machine learning, graph neural networks, dynamical systems modelling
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Multidimensional Omics Data Analysis You will be responsible for Setup a knowledge graph in neo4J for microbiome research Integration
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, storm activity, and other hazards using graph-based clustering, fuzzy machine learning, and reduced-order models – delivering scientific insight into where and when rerouting is needed. Real-Time Decision
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knowledge-graph groundedfactuality in LLM. The PhD students will work both independently and collaboratively within the group, and will have opportunities to engage with national and international partners
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prerequisite. Demonstrated motivation to work on AI-driven modeling of physical and chemical processes, including a genuine interest in graph neural network architectures and interatomic potentials. Experience
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of data handling, version control (e.g., Git), and reproducible scientific programming (desirable). Understanding of molecular representations (e.g., fingerprints, SMILES, graphs) and/or computational
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models, multi-view computer vision, semantic graph-based representations, and self-supervised learning—to automatically interpret and understand complex surgical procedures. The overarching goal is to