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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large
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recognitions and multi-class neural network algorithms. We propose to apply this emerging method to study samples from Europe, South Africa, and East Asia dated between 1.8 Ma and 60 thousand years ago (ka
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in several tasks within the project’s work plan, including: - Development of deep learning models (e.g., convolutional neural networks and vision transformers); - Presentation of results at consortium
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analysis in medicine. Experience of software version control with Git, typesetting with LaTeX, use of Linux computers; Experience with graph-based methods, and graph convolutional/neural networks; Experience
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improve data analysis techniques, e.g., goal selection, analysis of decision variables, and artificial neural networks. 7. Applicable legislation and regulations: Research Fellow Statute (EBI), approved by
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learn a monolithic, “black-box” world model, often using a large neural network as function approximators. While these models can be highly effective for prediction within their training distribution
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Groups: This position will work with two different laboratory groups. The first group is an interdisciplinary group exploring capabilities of artificial intelligence (AI) on different fields such as
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experimental research in social cognition, supplemented by computational approaches to understanding behavioural and neural mechanisms of social interaction, i.e., developing methods for analyzing behavioural
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learning. Theoretical areas of interest include (but are not limited to) optimization, neural networks, and reinforcement learning. Application areas of interest include (but are not limited to) robotics
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models using the latest deep neural network (DNN) libraries trained on state-of-the-art hardware. Working with data scientists to develop web applications for analyzing massive data sets, either in