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should be exclusively submitted through the consortium webpage https://raptor-consortium.com/recruitment/ RAPTORplus aims at adapting proton therapy treatment with multi-modality image information acquired
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on Graphs: Symmetry Meets Structure (LOGSMS). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing
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mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time-lapse data Proven programming expertise in
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. Stefanos Zafeiriou can be found at https://wp.doc.ic.ac.uk/szafeiri/ . Research Associate: A PhD (or be close to completion) in an area pertinent to the subject area, i.e. computer vision, machine learning
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intersectionality, and inclusion of underserved populations (men, students with disabilities, LGBTQ+ students, and students from immigrant communities). Excellent verbal communication skills. Interest in learning
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Columbia (UBC) Vancouver campus invites applications for a full-time Postdoctoral Research Fellow with expertise in artificial intelligence (AI), machine learning (ML), and data science. The position will be
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, and contribute to the growing field of Evolutionary Machine Learning powered by foundation models. V - Initial grant duration: 5 months, as long as it doesn't go beyond the end date of the project V.I
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this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model
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skills with Python. Desirable qualifications: Previous experiences with kinetic Monte Carlo. Previous experience with machine learning applications in molecular modelling, including experience with
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 8 days ago
-PULSE addresses a key open question in responsible AI: can we design practical machine learning systems that satisfy strong privacy guarantees [1] and fairness [2] constraints simultaneously, without