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domains. The successful candidate will: Develop algorithms to model team performance based on interpersonal (e.g., monitoring, communication) and cognitive (e.g., shared mental models) processes. Design an
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spatio-temporal generative models, and multimodal foundation models—including vision-language MLLMs and agentic AI frameworks—for longitudinal MRI and clinical data. Fellows will help build next-generation
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to the 4th February 2026. You will be investigating the safety and security implications of large language model (LLM) agents, particularly those capable of interacting with operating systems and external APIs
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shape the future through innovative education and ground-breaking research results, and based on the Arctic region, we create global social benefit. Our scientific and artistic research and education are
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based on the Arctic region, we create global social benefit. Our scientific and artistic research and education are conducted in close collaboration with international, national and regional companies
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for computing performance guarantees of autonomous AI agents under uncertainty, which will be integrated to various degrees into a use-case with self-driving shuttles. Based on your experience and interests, you
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/Project: We are building an optimisation-driven framework that (i) makes AI agents reliably operate advanced scientific software (e.g., DFT, Wannierisation, and quantum-transport codes) and (ii) uses
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, computational biology, physics or a related discipline, and have experience of agent-based and/or continuum modelling, analysis and simulation. You will have experience of programming in Python and C
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of agent-based and/or continuum modelling, analysis and simulation. You will have experience of programming in Python and C++, or demonstrated ability to rapidly acquire fluent knowledge of new programming
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in ecological fieldwork, ideally in wet grassland or agricultural systems - Skills in GIS, remote sensing, and spatial data analysis (bonus: agent-based modelling) - Demonstrated ability to work in