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Field
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hypotheses. This is a fantastic opportunity to contribute to world-class science in a leading biotechnology company. Who you are: Candidates must have a PhD in Computational Biology or Computational Science
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independently, has a passion for AI and its applications, and is willing to learn new technologies. The candidate should have a PhD in Computer Science or a closely related field. Relevant background and skills
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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include: · building hierarchical causal graphs to account for the multi-scale structure of the experimental system, · detecting latent variables that may affect causal inference
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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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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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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