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on methods, frameworks, and architectures that transform stakeholder information requirements into interoperable semantic models, ontologies, knowledge graphs, and digital twin services. Particular
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fine-tuned Vision-Language Models (VLMs) for game scenario detection, supporting zero-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking
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methods (e.g., filtering, factor graph optimization, moving horizon estimation) with learning-enhanced components, including meta-learning approaches for adaptive and generalizable estimation. The candidate
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through PoC to MVP. Experience with semantic web technologies; RDF, OWL, knowledge graph and ontologies for data harmonisation. Familiarity with dataspaces, metadata standards and data governance frameworks
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graphs (ARGs). Research areas include statistical/quantitative/population genetics, genealogical inference, machine learning, genetic prediction, genome-wide association studies, scalable linear mixed
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emphasis on Extremal and Probabilistic Combinatorics, Graph Theory, Discrete Geometry, Ramsey Theory and Combinatorial Number Theory. The initial appointment is for 1-2 years, with a starting salary of no
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opportunities within the company. Responsibilities Develop and implement advanced computational and machine learning strategies, including deep learning, graph-based methods, and probabilistic modeling
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and histology datasets. Applying graph neural networks, transformer models and generative AI approaches to study clone-microenvironment interactions. Integrating spatial transcriptomics, single-cell
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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population genetics/genomics. The focus of this postdoc will be on the application of Ancestral Recombination Graphs (ARGs) for spatial population genetic inference. Our work combines computational and