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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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HiPerBreedSim project. In this role, you will leverage recent advances in working with ancestral recombination graphs (ARGs) to develop algorithms and code for simulating population genomic data, including
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Learning, in particular Graph Neural Networks, Deep Reinforcement Learning, Generative Modelling, in particular Denoising Diffusions, Combinatorial Optimisation Commitment to Diversity The University
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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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, 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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written and verbal communication skills regarding research results. Preferred Qualifications: Experience with deep/graph neural networks and active involvement in data science and machine learning projects
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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 the boundary of model theory, group theory and geometry to develop new insights about definable groups, Diophantine problems (around Pila-Wilkie) and graph-combinatorial conjectures (such as
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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
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Condensed Matter Physics and Materials Sciences o Theoretical and Computational Biophysics o Soft Matter Physics o Physical Chemistry and Theoretical Chemistry o Combinatorics, Algorithm, Extremal Graph