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Field
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preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research results collected in accordance with the research protocols as stipulated. Prepare and present
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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a
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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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development experience GTSAM or similar factor graph optimization frameworks Field robotics deployment in challenging environments Multi-sensor calibration and fusion Commitment to open-source development and
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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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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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modelling and simulation of complex systems reinforcement learning or graph neural networks proficiency in Python and related computational toolchains a strong interest in interdisciplinary research bridging
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Information Modeling (BIM), including Revit, IFC, ifcopenshell, compliance checking, design generation, and design checking; and Experience in AI and LLM-related development, including RAG, knowledge graph
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, computational fluid dynamics and material science, dynamical systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics
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or graph neural networks proficiency in Python and related computational toolchains a strong interest in interdisciplinary research bridging project governance, systems thinking, and intelligent control