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, especially Eurocodes (EN 1991, EN 1992, EN 1993). Strong analytical skills and ability to validate structural performance through numerical methods. Excellent technical writing skills, including
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conducted in close collaboration with Everllence (formally known as MAN Energy Solutions). The developed methods have to be tested in simulation and on real engines. Previous Work https://openreview.net/pdf
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 28 days ago
(maximal total duration of each position: 24 months). It is mandatory to formalize applications with the submission of the following documents: i) B1 Form – Fellowship application (https://ist-id.pt/recursos
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met no later than the time the employment decision is made Strong written and verbal communication skills in English Experience in hydrometallurgy, chemistry and analytical methods. You are expected
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available at REQUIMTE. Main research field: Materials Engineering Admission requirements: The candidates must hold a PhD in Materials Engineering, Chemistry or similar scientific areas, obtained in
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, especially under high-performance applications. Traditional methods for coating and stabilizing LFP cathodes often face limitations in durability and electrochemical efficiency. This research aims to overcome
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a collaboration between Inria and Mitsubishi Electric R&D Centre Europe (MERCE) within the FRAIME project on artificial intelligence and formal methods. The project explores, on the one hand, how
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of questions, data, and analytic methods involved (1 page) a CV official documentation of a PhD degree or its equivalent a full list of publications two writing samples, at least one of which should be published
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position is a PhD in physics, computer science, statistics, or a related field. The candidate should: Be interested in statistical inference and methods, with a focus on the Bayesian approach. Possess solid
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statistically reliable AI, with applications to engineering. The research will focus on the design of formal reliability guarantees for black-box AI models operating under highly non-deterministic and context