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Field
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programming will be advantageous. Knowledge of intelligent decision agents based on graph neural network or similar will an advantage. Key Competencies Good knowledge in reliability analysis. Experience in
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electrical penetration graph (EPG) technology. The candidate will perform laboratory, greenhouse, and field studies, and to establish experiments to test different pest management strategies. Learning
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graphs (KGs). Contracting requirements: Presentation of the academic qualifications and/or diplomas, if applicable. Enrollment in a PhD degree program. Work plan: The fellowship holder will support WP2
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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Force Microscopy. Electroactive biomaterial experience, including electrochemical characterisation and synthesis. Expertise with advanced graphing and/or data analysis software (Prism, Origin Pro, Matlab
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visualization environments Optimize scene graphs, memory management, asset streaming, and runtime performance Contribute to research proposals and peer-reviewed publications Generative AI Integration Generative
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zero-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. Enable real
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evolution. Our work builds on experience developing pangenome graph construction and analysis tools (PGGB, ODGI, IMPG) and contributions to the Telomere-to-Telomere Consortium and Human Pangenome Reference
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Force Microscopy. Electroactive biomaterial experience, including electrochemical characterisation and synthesis. Expertise with advanced graphing and/or data analysis software (Prism, Origin Pro, Matlab
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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