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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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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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, 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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phenotypes, molecular biomarkers, and treatment responses from progress notes for the WONDER project. · Engineer semantic knowledge graphs and database query architectures capturing perioperative
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Apply Now How to Apply Applications should be sent to [email protected] and [email protected] with the subject line: Postdoctoral Application - Granular materials using graph theory. Interested
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environmental exposures and neurodevelopmental, behavioral, or health outcomes in children Knowledge of causal inference methods (e.g., propensity score analysis, directed acyclic graphs, mediation analysis
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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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fine-tuned Vision-Language Models (VLMs) for game scenario detection, supporting zero-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking
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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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-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference speed, compute efficiency, and scalability with concurrent agents. Maintain high