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in machine learning for continuous and discrete structures. The department has been growing rapidly in recent years. An inclusive and participatory environment are key elements in our growth. The 60
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develop privacy-aware machine learning (ML) models. We are interested in building models that are explainable and are extracted from complex and heterogeneous data. Within explainable ML, we are interested
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26th 2024. Project description and tasks Machine learning (‘artificial intelligence’) is having an immense impact on both society at large and research especially, and this impact is expected to increase
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of topics in modern machine learning research, including geometric deep learning, non-convex optimization problems and federated learning. In addition, this project is deeply connected with
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the development of new techniques for rehabilitation and functional impairments of injuries to the upper extremity supported by machine learning and neural networks. The position is an initiative within
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problem or what heuristics organisms might evolve. This research project is a collaboration between Eric Libby and Laura Carroll. It involves using machine learning techniques to infer the mechanisms by
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will be predominantly conducted in English. Experience in broad areas of expertise, including machine learning algorithm development, statistical methods, distributed learning, and cybersecurity, is
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etc.) Automated reasoning Machine learning Candidates are expected to have good knowledge in Artificial Intelligence and good programming skills in some of the following: Python, PyTorch library, OpenAI