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of analytical chemistry and machine learning. For more information please contact Prof. dr. Deirdre Cabooter, mail: deirdre.cabooter@kuleuven.be . Where to apply Website https://www.kuleuven.be/personeel/jobsite
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Computational Mechanics. Solid background in continuum mechanics and numerical modeling Strong interest in machine learning and scientific computing Experience with numerical methods for PDEs and data-driven
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Machine Learning at the University of Oslo invites applications for a doctoral research fellowship. The PhD candidate will work at the interface of machine learning, statistics, probability, and with
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. Additional qualifications Experience with one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models
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training programme at the start of the PhD to develop skills in areas such as programming, data analysis, machine learning and signal processing. This will provide the technical foundation required to work
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 15 days ago
dynamics data and advanced graph-based deep learning models to decode long-range communication pathways within macromolecular complexes. The PhD candidate will play a central role in this effort by
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! Recent advances in EO, such as dense satellite time series, rapid data access, and machine learning, have enabled the detection of deforestation, fires, floods, agricultural activities, and land cover
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harvesting recent breakthroughs in Machine Learning (ML) and analytical modelling. Specifically, this project seeks to quantify key performance metrics and create powerful adaptive ML-driven management methods
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combines machine learning, legal applications, and empirical evaluation in collaboration with judicial partners. The project offers a unique opportunity to work on real-world, high-stakes AI systems in
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samples, phase retrieval in this regime remains challenging, limiting multiscale imaging approaches in near-field holotomography. To address this, the PhD project combines machine learning, high-performance