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- Delft University of Technology (TU Delft); yesterday published
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Field
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. Applicants with training in quantitative and empirical research and experience in requirements engineering, safety-critical systems, or AI/ML/LLMs/Knowledge Graphs are especially encouraged to apply. This PhD
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the connections between clouds and climate. Ultimately, we want to create to causal graphs for large-scale cloudiness, its dependence, and its effect on the related environmental factors. Additional or alternative
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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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including: * Algorithmic game theory * Approximation algorithms * Automata and formal languages * Combinatorics and graph algorithms * Computational complexity * Logic and games * Online and dynamic
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graph, and discrete random processes. The aim of this project is for the student to develop an understanding of these tools and to apply these techniques to open research problems in the field. Entry
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families (e.g., generative models or graph/equivariant neural networks) to accelerate candidate discovery and hypothesis generation. Disseminate research findings through publications, conference
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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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projects on, e.g., AI security, linguistically motivated NLP, and knowledge-graph groundedfactuality in LLM. The PhD students will work both independently and collaboratively within the group, and will have
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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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areas, including generative modelling (e.g. diffusion models, flow matching, self-supervised and autoregressive approaches), causal machine learning, graph neural networks, dynamical systems modelling