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for a motivated Research Associate with skills in Machine Learning to join our team working in the School. You will contribute to UNSW’s research efforts by developing advanced machine learning models and
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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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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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the National Archives website at http://www.archives.gov/veterans/military-service-records/ *Please Note: As part of the first round of screening, the committee will conduct an anonymous review
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construction, and phylogenetic placement Ability to assess where machine-learning approaches may complement existing bioinformatic and phylogenomic methods, particularly for improving taxonomic resolution Skills
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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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level. Successful candidates will teach and supervise students who are serving officers and civil servants in the UK and allied armed forces and partners. There are also opportunities to contribute
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expertise in machine learning, soil microbiomes, microbial 3D printing and biophysics, our team has access to a broad spectrum of techniques and practical know-how. This is therefore an exciting opportunity
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offers excellent researchers who have recently completed their PhD the chance to continue their research career at CTU. Fellows receive a two year fellowship and become members of a team led by a mentor
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paradigms centered on human perception. Finally, the recent rise of foundation models and multimodal artificial intelligence opens up new perspectives at the interface between coding and machine learning