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/przydatne-dokumenty/ Other working conditions Workplace: Interdisciplinary Centre for Mathematical and Computational Modelling Career opportunities: more information: https://rekrutacja-i-rozwoj.bsp.uw.edu.pl
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reinforcement learning for large language models (LLMs). Research directions include developing next-generation post-training algorithms, exploring diffusion-based approaches to reasoning with language models
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. • Share concerns appropriately by avoiding direct confrontation in staff meetings: supporting in public, confronting in private. • Participate in RA selection and evaluation processes. • Role model
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data-driven methods (optimisation, generative AI, agent-based modelling, machine learning). Our work provides decision support for policy makers, industry stakeholders, and researchers by delivering
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: The selected candidate will work within the scope of Activity 1 of the project, specifically on the personalization of AI assistants based on small generative language models (SLM) and on mechanisms
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 22 days ago
distance of their assigned duty station. The Data-Driven EnviroLab (DDL) is an interdisciplinary and international research initiative based at UNC’s IE that is redefining how data is used to tackle
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
distance of their assigned duty station. The Data-Driven EnviroLab (DDL) is an interdisciplinary and international research initiative based at UNC’s IE that is redefining how data is used to tackle
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of cutting-edge AI4EO technologies — spanning application-specific AI models, generative models, foundation models, and autonomous AI agents — with applications ranging from ground-based analytics to onboard
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training an autonomous agent to ‘learn’ a control strategy. This formalism is similar to that of optimal control, with the difference that the agent does not have an explicit model of the dynamics
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-informed neural networks (PINNs) for integrating mechanistic constraints into ML frameworks, and creating LLM-based agents to assist with mechanistic model construction and knowledge curation. Track B