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Field
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structural and algorithmic graph theory. The purpose of the role is to contribute to the project “Algorithmic meta-classifications for graph containment”, working with Professor Matthew Johnson, Dr Barnaby
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. Investigate and build robust data and AI agent pipelines for continuous learning and knowledge acquisition, including annotation strategies and knowledge graph development for aquaculture stress events. Design
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funding. Requirements A PhD degree in a relevant discipline (e.g., civil engineering, computer science, AI, architecture, or related fields); A strong publication track record; Experience with Building
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and experience: Essential criteria PhD in bioinformatics, computational biology, or a related discipline * Extensive experience and expertise in analysing/ training models on biological or chemical
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: Developing and deploying machine learning models (e.g. graph neural networks, neural force fields, diffusion models) for molecular property prediction and molecular generation. Integrating quantum chemistry
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commercially orientated research projects in computer vision and machine learning. To be successful you will need: A PhD in Computer Science, Engineering or other Machine Learning-related field. Programming
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on “Maternal Immune Activation” involving the development of novel artificial intelligence methods (graph and geometric deep learning, LLMs, …) working on methods for predictive multi-omics integration
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:00pm Job Summary: The Postdoctoral Fellow provides for an internship and continuation of scholarly activity and research after achieving the PhD or other doctoral degree under the direction of a senior
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PhD in Computer Science, Engineering or other Machine Learning-related field. • Programming experience in python, C++ or other relevant language and experience in deep neural networks • Strong
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to develop a knowledge-aware and event-centric framework for natural language understanding, in which event graphs are built as reading progresses; event representations are learned with the incorporation