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degree in mathematics, strongly encouraged to apply. Experience and demonstratable knowledge in deep learning and one or more of the following: transformer networks, implicit neural functions, graph neural
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workflows — including methods for knowledge graph construction, advanced querying, and data quality assurance. Contribute to aligning the developed methods with emerging standards for information modelling
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regarding research results. Preferred Qualifications: Experience with deep/graph neural networks and active involvement in data science and machine learning projects. Experience in multimodal data fusion (e.g
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learning and one or more of the following: transformer networks, implicit neural functions, graph neural networks and/or probabilistic graphical models; and causal inference. • An outstanding publication
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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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Fellow. The Research Fellow will be expected to conduct research on algebraic geometry pertinent to moduli space of curves in the context of dual graphs and del Pezzo surfaces, SUSY curves, and category
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representation methods for accelerated inverse design Large language, diffusion & graph neural models for materials discovery Fine tuning and architecture optimisation of foundation models Inverse design of next
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. Demonstrated high level of written and oral communication skills. Preferable Experience in eukaryotic cell culture/tissue culture Expertise with advanced graphing and/or data analysis software (Prism, Origin Pro
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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