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-off companies. CONTEXT AND MISSION We are seeking a postdoc to join the Quantum Machine Learning team (QML-CVC) in beautiful Barcelona. The QML-CVC team (https://qml.cvc.uab.es /) is part of
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
PhD candidate to develop and apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular
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machine-learning scripts that feed into these pipelines. You execute and monitor these scripts, then integrate their output into project datasets. Throughout this work, you maintain clear documentation
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Shrivenham and will undertake high-quality scholarship and support a range of professional military and security education courses at the postgraduate level. Successful candidates will teach and supervise
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for the PhD admission is available at TalTech´s web-page: https://taltech.ee/en/phd-admission The following application documents should be sent to tarmo.soomere@taltech.ee CV Motivation letter Degree
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Computational Mathematics for reliable and trustworthy uncertainty quantification in science, engineering, and machine learning. Your workplace You will be employed at the Division of Applied Mathematics in a
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of everyday life. This project aims to change that by developing AI-driven methods to assess wellbeing through video-based sentiment analyses. As a PhD student, you will develop and refine machine learning
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised
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, funded by a Leverhulme Trust Research Leadership Award held by Dr Alessio Spurio Mancini. ECLIPSE's goal is to develop next-generation inference frameworks that combine machine learning with rigorous
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composites for enhanced durability, performing microstructural analysis and mechanical testing. Topology Optimization & AI Integration: Use AI and machine learning to guide structural and topology optimization