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techniques (Transformers, Recurrent Neural Networks, and other neural network architectures). All the previous points focusing on the research and deployment of post-training strategies, including instructions
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interdisciplinary areas. Research fields of particular interest include, but not limited to: biomedical science and engineering veterinary science computer science and data science neuroscience and neural
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neural networks, fine-tuning techniques, and inference and optimization. Writing academic reports Delivering high-quality work at a fast pace, understanding the importance of time and speed in a startup
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, including deep neural networks and relevant frameworks * Documented several years of experience in development with Python and version control systems, e.g., Git * Documented experience in large-scale data
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, innovative technologies for biomass conversion, neural network systems, and artificial intelligence for more efficient mathematical and computational approaches. Subject description The work focuses on
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expertise in artificial intelligence and a demonstrated interest in advancing qualitative research methods in social sciences through interdisciplinary collaboration. We primarily seek to appoint
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software such as recommendation systems, computer-aided decision support systems Previous experience with using deep learning models (e.g., convolutional neural networks, autoencoders, transformers
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, artificial intelligence, and cosmology. Research Opportunity These positions are an opportunity to join Professor Benjamin D. Wandelt as he establishes a new research group at Johns Hopkins University
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., decision trees, logistic regression, Naive Bayes, SVM); Artificial neural networks and deep learning; Dimensionality reduction (e.g., PCA, Johnson-Lindenstrauss); Classical methods for clustering (e.g., k
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workings of black-box machine learning models such as deep neural networks, they have severe drawbacks and limitations. The field of interpretable machine learning aims to fill this gap by developing