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and synthesis of new materials. You should have a PhD in a relevant field (Computer Science, Mathematics are most likely to fit the role, but we are open to Chemistry, Materials Science, Chemical
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PhD in a relevant field (Computer Science, Mathematics are most likely to fit the role, but we are open to Chemistry, Materials Science, Chemical Engineering, etc.), expertise in cutting-edge AI and
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PhD in a relevant field (Computer Science, Mathematics are most likely to fit the role, but we are open to Chemistry, Materials Science, Chemical Engineering, etc.), expertise in cutting-edge AI and
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background in systems thinking, analysis and modelling Experience in teaching and supervision in higher education at least on MSc level Knowledge of graph theoretical approaches and graph signal processing
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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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- $83,468 - $113,262 p.a. plus 17% super Contribute to innovative research in algebraic graph theory. Work with world-class mathematicians. Investing in you - benefits package including salary packaging
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continuation of scholarly activity and research after achieving the PhD or other doctoral degree under the direction of a senior faculty member who serves as a mentor for the postdoctoral appointee. Additional
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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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position focuses on advancing the integration of gene regulatory network (GRN) simulations into multicellular and tissue-level systems using machine learning—particularly graph neural networks (GNNs) and
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on advancing the integration of gene regulatory network (GRN) simulations into multicellular and tissue-level systems using machine learning—particularly graph neural networks (GNNs) and reinforcement learning