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challenge of how to predict and reconstruct the evolution of particle-laden turbulent flows through scientific machine learning paradigms. You will also be involved in the data-generation and data curation
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Documented ability to work in Python Experience with machine-learning methods for record linkage and text analysis Documented experience with machine-learning methods for image-to-text transcription
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the background we imagine would best fit the role. Even if you do not meet all the requirements and feel that you are up for the task, we absolutely want to see your application! The PhD process is a learning
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Heidelberg Institute for Theoretical Studies (HITS gGmbH) | Heidelberg, Baden W rttemberg | Germany | about 13 hours ago
) in Geometric Deep Learning to join the Machine Learning and Artificial Intelligence (MLI) g roup to perform research in geometric deep learning for materials science. This research is part of
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University, the Communicable Diseases Agency Singapore (CDA), the National Environment Agency Singapore (NEA), the Machine Learning & Global Health Network (MLGH), and wider regional and global partners. Key
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selection procedure. For more information see https://www.tudelft.nl/onderwijs/opleidingen/phd/admission You enjoy performing research. You are independent, self-motivated and eager to learn. You are keen to
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intelligence, or related areas Experience with large language models, machine learning systems, software testing, program analysis, requirements analysis, or software quality assurance Strong programming skills
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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 with large acoustic datasets and
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generative AI framework that utilizes machine learning predictions and quantum chemistry simulations to design stable, synthesizable, high-performance molecules. The framework will integrate multi-objective
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existing models struggle to capture this complex, multiscale phenomenon efficiently. This project will develop a novel, physics-informed surrogate model using Bayesian machine learning to predict gas