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Field
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with demonstrated ability to implement and optimize AI/ML models for biomedical datasets. Preferred Knowledge, Skills and Abilities Mathematical Modeling: Strong foundation in numerical modeling, graph
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text leveraging fine-tuned Vision-Language Models (VLMs) from WP3, supporting zero-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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trials, and treatment guidelines, and transform these into structured knowledge graphs encoding relationships among histotypes, biomarkers, therapies, and outcomes. Assess the accuracy, completeness, and
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-dimensional biomedical datasets, including transcriptomics, proteomics, secretomics and other molecular data Apply pathway, network, graph-based and mechanistic modelling approaches to unravel adverse effects
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extreme weather to cybersecurity threats. Working within the LDTRC, you will undertake a range of research tasks, including: 1) Defining the ontology and knowledge graph architecture for a scalable digital
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apply machine learning and deep learning models (e.g., graph neural networks, generative models, transfer learning) for materials property prediction, interpretation, and inverse design. Perform high
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extreme weather to cybersecurity threats. Working within the LDTRC, you will undertake a range of research tasks, including: 1) Defining the ontology and knowledge graph architecture for a scalable digital
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-Resilient Encryption, Privacy-preserving Financial Investigations, Graph Analytics, Criminal Network Discovery, Suspicious Activity Detection, Digital Content Provenance and Authenticity, etc., which
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scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference speed, compute efficiency, and scalability with concurrent agents. Enable real-time adaptive learning