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within the framework of project FLUIDNET: A Novel Deep Learning Architecture for Solving Viscoelastic Fluid Flows with reference 2024.13980.PEX, f, funded by national funds through FCT/MCTES, under
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-liquid critical point. The dynamic nature of the degree of miscibility renders such fluid pairs to be applicable in several applications such as liquid-liquid mass transfer, flow pattern tuning to name a
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) A PhD in a related discipline, and/or relevant work experience. Experience in at least two of the following areas: engineering design, thermodynamics calculations, heat transfer, fluid flow, radiation
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objectives. Research activities span novel and alternative working fluids, single- and multi-phase flow phenomena, and thermal processes relevant to low-carbon energy technologies. Current research focuses
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capability in the field of thermal fluid engineering is being sought. https://www.toyota-ti.ac.jp/research/laboratory/post-42.html [Work content and job description] (Immediately after appointment) ・Promoting
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Infrastructure? No Offer Description This project is part of the European ERC Synergy project Karst https://erc-karst.eu/ , which aims to develop a predictive flow model for an entire karst network. We will
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mathematics and learn more about modeling of atmospheric or oceanic flows, or the motion of charged fluids such as plasmas? We are looking for a Doctoral student to become part of Klas Modin's group
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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply
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smart is AI currently and how intelligent will it become?”. However, we do not know because current methods for evaluating intelligence in people are not suitable for AI and vice versa due to inherent
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environments, geophysical fluid, and geodynamics. The successful candidate will be expected to belong to the Fluid Geophysics Group, and handle education and research guidance in graduate and undergraduate