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text leveraging fine-tuned Vision-Language Models (VLMs) from WP3, supporting zero-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference
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reasoning Vision-Language Models (VLMs) Multimodal representation learning Self-supervised and weakly supervised learning Fine-tune and adapt Foundation Models and Vision-Language Models for domain-specific
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of Cambridge: Classics, Geography, History and Philosophy of Science, Modern and Medieval Languages (including Theoretical and Applied Linguistics), Music, Philosophy. Further information about these Faculties
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create. Our specialities span across a range of subject areas, including history and global cultures, English language and linguistics, journalism, and philosophy. Together, our unique community of experts
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collaborative research and experimentation. The candidate with the following knowledge is preferable: Agentic AI, large language models (LLMs), and Vision-Language-Action (VLA) models. Preference will also be
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or reproducible computational pipelines (desirable) You will bring Essential Develop, apply, and iteratively refine protein/genomic language models to design novel protein variants informed by evolutionary and
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involving at least one of the subjects listed above (for example, Computer Science and Linguistics) should apply to this competition and not the Arts, Humanities and Social Sciences competition
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autocratic politics, democratic resilience, and/or anti-autocratic movements in Africa. It would be advantageous if you were fluent in French, Luganda, Portuguese, and/or another language spoken in Africa, so
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of 1800 to the present day) * Linguistics (theoretical and applied) Modern and Medieval Languages (limited to French and Francophone, German, Italian, or Slavonic) * Theology The first of the two posts is
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agents into gaming scenarios (e.g., gameplay, interactions, procedural content generation, dynamic narratives) Leverage fine-tuned Vision-Language Models (VLMs) for game scenario detection, supporting zero