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Field
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Number 10267010007 Is the Job related to staff position within a Research Infrastructure? No Offer Description Mission: Develop a doctoral thesis on the mechanistic interpretability of graph neural
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into insight). To tackle this, the research blends ideas from knowledge graphs, stream processing, database theory, logic, edge and cloud computing, and Artificial Intelligence into a single coherent framework
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properties are proved, refuted by counterexample, or reported as an explainable gap. All of it is written in a domain-specific language whose syntax trees and graphs we keep in a projectional editor (JetBrains
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models ranging from baseline approaches to graph neural networks. You will also oversee the open release of project datasets, models, code and documentation. The successful candidate will join Oxford's
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Title: AI-Augmented Procedural Node-Graph Authoring for Technical Artists PhD Studentship: R-LINCS2 funded. The Studentship is available for a February start. A PhD studentship that comprises tax-free
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or hard constraints, operational bounds (voltage limits, capacity, phase balance), and network topology through graph neural network architectures. You will build validated benchmark datasets and an
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(Kirchhoff's laws) as soft or hard constraints, operational bounds (voltage limits, capacity, phase balance), and network topology through graph neural network architectures. You will build validated benchmark
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(Kirchhoff's laws) as soft or hard constraints, operational bounds (voltage limits, capacity, phase balance), and network topology through graph neural network architectures. You will build validated benchmark
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knowledge graph from scientific papers and cognitive test questionnaire data, and second, to integrate the graph with transformer-based large language models and causal learning. This offers an explainable
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; Build data and knowledge infrastructures, namely knowledge graphs, ontologies and multimodal representations of the CENSE scientific body; Collaborate with the CENSE team and external partners in