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, mythical scenes of making, or depictions of making in sacred or military contexts). They will take into account (where applicable) how images interact with epigraphic frames and contexts of production and
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of making in ancient Roman visual culture (e.g. depictions of craftsmen at work, mythical scenes of making, or depictions of making in sacred or military contexts). They will take into account (where
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analyzing textual and visual content with supervised machine learning. Conducting focus groups with social media users to understand how this content may shape beliefs and attitudes about AOMs, a healthy diet
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), machine learning, network analysis, and econometrics. Proven ability using industrial ecology methods, such as LCA, MFA, and (MR)IOA to assess the scale and drivers of social, economic and environmental
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programming skills in Python (experience with MATLAB or R is a plus). Proven experience with deep learning and machine learning frameworks (e.g., TensorFlow, PyTorch). Background in computational modeling
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the Molecular and Materials design program (MMD Hub ) of the Faculty of Science at UvA. What are you going to do? The aim of the project is to use advanced Machine Learning techniques to predict the anharmonic
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with strong expertise in one of these categories: solid-state NMR; Quadrupolar solid-state NMR; Automated NMR analysis & machine learning; Lipid biochemistry (and chromatography knowledge in general
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development and pipeline development and deployment competence Expertise in biostatistics, including machine learning and AI Previous experience in teaching, student supervision, and course development is
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machine learning or computational statistics or are eager to learn. Experience or affinity with constructing basic electrical circuits is a plus. You flourish in a team-centered, multicultural and
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affiliated knowledge institutes. Key areas of interest include Bayesian machine learning, probabilistic graphical models (factor graphs) and probabilistic programming. Where to apply Website https