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Field
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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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Process: designing, preparing, shaping and characterizing materials in order to discover, control and optimize specific functions. The ICMCB carries out fundamental research on model materials and/or
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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
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either or both agent-based modeling and social network analysis/network science. The ideal candidate should be able to program in C or C and have experience working with tera-bytes of data. Preference
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effectiveness of therapeutic agents and utility of biofluid biomarkers in pre/post-blast animal models, human volunteers and victims of blast exposure. It is fully anticipated that the multi-faceted experiences
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during verbal interactions with human and AI agents. You will be based within the Section for Cognitive Systems, which is a lively and research oriented group of scientists and support staff with a broad
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Scene Synthesis: Re-design diffusion and NeRF-style models so multiple agents jointly reconstruct a scene. Cooperative Scene Understanding: Re-design semantic segmentation and object-detection models in a
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transplantation using mouse animal models. The research involves understanding the mechanisms underlying resistance/relapse following immunotherapy such as CAR T and developing novel agents for protecting
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language processing (NLP), and fine-tuning techniques Familiarity with structured reasoning, chain-of-thought processes, and agent-based systems is beneficial Strong programming skills (preferably Python); experience
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of computing and healthcare. Methodologies of interest include: Multi-modal learning Foundation models, including large language models Agentic AI Multi-agent AI systems Transfer learning Self-supervised