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, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines Language requirements English C1 or equivalent Application deadline January, please see website for exact
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boundaries of system-level modelling, analysis, design, exploration and synthesis beyond the current state-of-the-art? Or are you curious to learn more about the application of AI for system design? We
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
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and machine learning. Internal further training & coaching: The Vienna Doctoral School as well as the Department of Human Resources offer plenty of opportunities to grow your skills in over 600 courses
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of visualisation, machine learning, and human-computer interaction under the joint supervision of both institutions. The position is shared by TU Wien and USTP and offers the opportunity to conduct research at both
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English. A basic training in one of the following fields; polymer synthesis, polymer characterisation, machine learning or high throughput experimental platforms will be of advantage. Admission Regulations
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-intensive physics with nuclear/particle aspects, advanced detector R&D, machine learning and AI and emerging computational methods in quantum computing. The position is intended for an excellent and broadly
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. You will become part of a dynamic, collaborative working environment with expertise in drilling engineering, geomechanics, machine learning, and energy systems. The project will integrate real‑time
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
. Picchini. Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings. Transactions on Machine Learning Research, 2024 Kugler, F. Forbes, and S. Douté. Fast
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understand, explain and advance society and environment we live in. Your role The University of Luxembourg invites applications for a fully funded Ph.D. position in machine-learning force fields (MLFFs