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related to modeling and simulation of biological systems, 3) very good IT skills, in particular the ability to program in Python, 4) very good knowledge of machine learning methods, neural networks, and
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools
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- excellent command of English (spoken and written), sufficient for preparing scientific publications and presenting research results at conferences, - knowledge of machine learning methods, - sufficient
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/Qualifications Requirements: • Master’s Degree in Computer Science or Mathematics or Physics or a relevant discipline. • Background in machine learning or programming
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learning, computer vision, or a related field; knowledge of affective computing, generative AI models, and deep-learning methods; proficiency in Python and experience with machine-learning libraries
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or an equivalent qualification to have a PhD degree to have a very good command of English to have scientific interests related to Artificial Intelligence and Machine Learning to have outstanding
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to density functional theory (DFT) calculations and the generation of high-quality atomistic datasets for the development of analytical bond-order potentials (ADP) and machine-learned interatomic potentials
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for the aforementioned reactions. Based on the automatic reactor, an I-TR-NUS&TR-L running benchtop spectrometer, and machine learning algorithms, we will be able to achieve a higher yield and control the reaction to stop