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Field
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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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other life-threatening illnesses. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases
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to the roles of political economy, policy, regulation and business models in resource and energy use in production, consumption or both (especially in housing and cars) or equivalent experience gained through
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robotic manipulation. This role will support our mission to advance AI-driven robotic systems for real-world applications, including human-robot interaction, robotic perception, and intelligent control
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of unknown PFAS, supporting agricultural research and advancing the ARS mission. The project will involve compiling high-resolution mass spectrometry (HRMS) databases for PFAS, creating machine learning models
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such as large-language models and vision-language models for high-throughput analysis of video data. For NeuroAI focus: Apply data-driven dynamical system modeling tools using specialized software packages
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-driven and machine-learning models. The role will also involve conducting materials synthesis and electrochemical studies, troubleshooting and improving automated workflows, analyzing research data, and
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28th September 2026 Languages English English Norsk Nynorsk English PhD Research Fellow in Data-Driven Computational Mechanics for Offshore Wind Energy Apply for this job See advertisement UiB
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predictive control (MPC), data-driven approaches, and physics-informed models. Perform dynamic simulation and techno-economic and environmental assessment under real operating conditions and uncertainty
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timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization? We are particularly interested in: LLM-driven