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). (Required) Experience developing workflow automations using tools such as Qualtrics or Microsoft Power Automate. (Preferred) Familiarity with AI and large language model (LLM) platforms (e.g., OpenAI GPT
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both synthetic and real-world datasets. Synthetic benchmarks will be generated using established generative models capable of producing ground-truth synchronous patterns under varying conditions to
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of science to society. As a modern employer, it offers attractive working conditions to all employees in teaching, research, technology and administration. The goal is to promote and develop their individual
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, including but not limited to: Azure AI services, Copilot Studio and related tools for building chatbots, agents, and automated workflows, on-premises AI/LLM servers and open-source models, including
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related to climate change and marine biogeochemistry. Expertise in modeling climate variability and extreme weather events, along with their impacts on ecosystems and public health. This position is subject
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. The scholarship applicant must meet the following conditions on the date of application: be no more than 7 years since obtaining the first Ph.D. degree, Ph.D. studies at a university outside the Czech
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for an oscillating water column wave energy conversion power plant, funded by IDMEC project “PSalerno”. Applications will be considered under the following conditions: Scientific Area: Mechanical Engineering Admission
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safety: Reactor Kinetics, the study of how neutron population, and therefore reactor power, evolve under transient conditions. These dynamics drive the balance between heat generation and removal, which is
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to weather patterns and extreme events. Despite their importance, critical controlling processes remain poorly understood, underscoring the urgent need to improve Earth system modeling. Significant
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, autonomy, and ability to work effectively in a team. - Strong motivation to contribute to the development of next-generation climate models. Website for additional job details https://emploi.cnrs.fr/Offres