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National Institute of Research and Development for Electrochemistry and Condensed Matter | Romania | about 1 month ago
performing numerical simulations (e.g. FEM analysis), mechanical modeling, and experimental validation of mechanical components and systems. Basic experience in laboratory work, data analysis, technical
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molecular simulations, and cutting-edge AI techniques including graph neural networks (GNNs) and large language models (LLMs) to accelerate experimental design and discovery of novel materials. The research
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of doctoral degree certificate or documentation that clarifies when the degree of doctor is expected to be obtained, Verified copies of other diplomas, list of completed academic courses and grades, Copy
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Job related to staff position within a Research Infrastructure? No Offer Description Description of the workplace The position as post doc (3 years) in human rights, with focus on digitisation and the
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demonstrating clinical techniques, guiding experiential learning, and promoting safe and effective practice using simulation models, cadavers, and live animal experiences. The instructor contributes
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unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position The Structural Mechanics group (https://www.ntnu.edu/kt/research/sm ) is
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into account the career breaks specified in the definition of a post-doc position in the Regulations on Awarding Funding for Research Tasks Funded by the National Science Centre for research projects, post
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with the bioinformaticians at IBMM and will act as a bridge between the simulation specialists and the experimentalists within the consortium. Where to apply E-mail bernard.hehlen@umontpellier.fr
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hydrodynamic load models. The purpose of this work is to increase the understanding of longlines with seaweed under sea loads and develop accountable hydrodynamic load models. Your supervisor will be Professor
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models