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fair access to opportunities (employment, healthcare services, education) and mitigating spatial inequalities; - develop (deep) learning models for spatial structures and dynamic graphs to support the
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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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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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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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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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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; 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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(sequence-based, graph-based or descriptor-based). The fellow will also design and implement evaluation procedures based on relevant properties (predicted activity, stability, sequence diversity) and will
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molecular simulations, and cutting-edge AI techniques including graph neural networks (GNNs) and large language models (LLMs) to accelerate experimental design and discovery of novel materials. The research