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Methodology The PhD candidate will develop innovative AI models using machine learning and deep learning frameworks. Methodologies will include supervised and unsupervised learning approaches to identify and
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invites applicants for four PhD Fellowships in subsurface characterization within geosciences, reservoir engineering, molecular modelling, and machine learning at the Faculty of Science and Technology
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before application deadline. Strong programming skills and experience with AI, computer vision, image analysis and deep learning are advantages. Knowledge of hematology, cytology and pathology is a plus
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to calculate your points for admission. Emphasis is also placed on your: background in algebraic or symplectic geometry or mathematical physics programming skills and experience with computer algebra packages
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or more of the following empirical research methods will be considered an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the
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; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural Language Processing and LLMs; R; Python. Applicants must be fluent
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an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural
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an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural