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models and reinforcement learning models for 3D graphs of materials to explore vast inorganic chemical spaces and design synthesizable energy materials. You will couple such models with physics simulation
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federated knowledge graph framework that facilitates the querying, consolidation, analysis, and interpretation of distributed proteomics-focused clinical knowledge graphs. To achieve this, we will employ
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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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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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models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train these methods in a closed-loop
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XAI methods, e.g. counterfactuals in reasoning and knowledge graphs (KGs) based on domain expertise, to strengthen inferences drawn from data, and to reduce complexity of learning – by factual reasoning