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Research Fellow in Fractional Edge Decompositions of Graphs Job No.: 687653 Location: Clayton campus Employment Type: Full-time Duration: 2-year fixed-term appointment Remuneration: $83,280
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to the School's research efforts in combinatorics. This role involves working on a project concerning fractional edge decompositions of graphs, addressing well-studied open problems, and exploring related variants
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/knowledge graphs, and carbon accounting. The Research Fellow will help develop and lead the CognitionX Lab (https://cognitionx-lab.github.io/ ) with Dr. Jinying Xu, Assistant Professor and Director of
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Responsibilities: Conduct programming and software development for graph data management. Design and implement machine learning models for optimizing graph data management. Conduct experiments and evaluations
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Responsibilities: Research and develop novel ML-based methodologies and algorithms in LLM-empowered Sub-Graph Learning for Large Graph Models. Working closely with other Postdoc/RA/PhD students to discuss the ideas
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in the following areas: Deep Learning, Scientific Machine Learning, Stochastjc Gradiant Descent Method, and Numerical PDE’s - Advised by Dr. Yanzhao Cao Probabilistic Graph Theory (Network Traversal
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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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Optimized Tissue Culture and Single-Cell Omics Digital Composite Manufacturing Multi-Element Alloys Resistant to Hydrogen Graph based inverse reinforcement learning (IRL) framework for analyzing and
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the system, converting mapped graphs to instruction sequences, and execute test programs ; Collaborate in writing scientific articles to disseminate results. ; 4. REQUIRED PROFILE: Admission requirements
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computing environments. Data presentation skills to generate high-quality graphs, plots and figures suitable for scientific publication is also an essential criterion for this role. Customer advert reference