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, and clinical safety datasets Implement graph-based retrieval-augmented generation (RAG) methods to enhance knowledge extraction and information synthesis Develop cross-pathway analytical methods using
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no more than two additional pages of tables, references, and graphs, describing the proposed research for the fellowship year. The names and email addresses of 2 referees, who will be asked via a
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for spatiotemporal data (e.g., CNNs, LSTMs, Transformers, or Graph Neural Networks). Hybrid modeling: Experience with physics-informed machine learning or the integration of ML with data assimilation/multivariate
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on energy-efficient circuit design and software-hardware co-optimization, with exciting applications in graph-based prediction. What we’re looking for: A PhD in Electrical and Computer Engineering or a
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narratives) Leverage 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
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knowledge graphs, rules, and process understanding, with implications across sectors from ecology to infrastructure. 4. Theme 4 (“Communities”): Green and Resilient Communities and Entrepreneurship
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driven inverse design of functional materials. Current research directions include: Reversible material representation methods for accelerated inverse design Large language, diffusion & graph neural models
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, group theory, and/or graph theory will be necessary. Experience in modelling biological processes, and in algorithm development or computation will also be valuable. Proven commitment to proactively
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-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference speed, compute efficiency, and scalability with concurrent agents. Maintain high software engineering
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advanced light microscopy data. The lab’s research scope ranges from reinforcement learning for drug design, interpretable ML pipelines for cancer research and diagnosis as well as graph neural networks