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Field
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modelling and simulation of complex systems reinforcement learning or graph neural networks proficiency in Python and related computational toolchains a strong interest in interdisciplinary research bridging
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Shai Evra Graph theory, representation theory, number theory Adi Glucksam Complex analysis, potential theory, and dynamics Or Hershkovits Geometric analysis Mike Hochman Dynamical systems
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or graph neural networks proficiency in Python and related computational toolchains a strong interest in interdisciplinary research bridging project governance, systems thinking, and intelligent control
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on “Maternal Immune Activation” involving the development of novel artificial intelligence methods (graph and geometric deep learning, LLMs, …) working on methods for predictive multi-omics integration
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for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a multidisciplinary team Evidence of strong communication and scientific
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quantum information theory, algebraic topology, polyhedral combinatorics, graph theory, and optimization are strongly encouraged to apply. For more details on the project, visit open positions page . To be
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development experience GTSAM or similar factor graph optimization frameworks Field robotics deployment in challenging environments Multi-sensor calibration and fusion Commitment to open-source development and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
with biomedical data, including clinical, EHR, omics, and imaging Knowledge graphs (KGs), and integrating LLMs with KGs Multimodal LLMs Special Physical/Mental Requirements Special Instructions
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Lab (MaTRIX Lab) develops advanced computational and AI methodologies to decode complex biological systems and accelerate discoveries into translational impact. The lab integrates deep learning, graph
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accuracy in link-tracing designs (e.g. Respondent driven sampling) Partial graph data collection strategies for networks (e.g. Aggregated Relational Data) Large scale models for anomaly detection on graphs