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expression data and/or neural data); Demonstrable teaching experience; Preferably expertise in the use of machine learning techniques for pattern recognition, such as dimensionality reduction, clustering, and
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, including the application of machine learning tools. You also have an eye for peculiarities, work accurately and are critical of your research. You will work in a lively and stimulating environment with other
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Apply now The Faculty of Science and the Leiden Institute of Advanced Computer Science (LIACS) are looking for: Assistant Professor in Machine Learning for Quantum Systems (0.8-1.0 fte) The applied
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networks. Modelling and control of linear parameter varying systems. Spatial-temporal multi-physics systems and model reduction. Data analytics, machine learning, artificial intelligence. Constrained and
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machine learning; a minor in mathematics is a plus; A strong interest in data mining research with focus on local pattern mining, exceptional model mining, or related techniques. Data mining software
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experience and profile: MSc in Computer Science or equivalent Experience in setting up computer and network infrastructures a motivational teacher, with an encouraging teaching style; experience in supporting
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for compositional methods that focus on extra-functional aspects, such as performance, resource budgets, security, or energy, pursuing hybrid, knowledge-driven and machine-learning-based, approaches. As a newly
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Requirements You must: have completed a Master in Artificial Intelligence, Computer Science including AI courses, or a related Master programme; have experience with both machine learning and symbolic AI; be
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, machine learning, and large-scale data analytics. You will work closely with the advisors to define, develop, and execute your own research. The Ph.D. dissertation will be defined by you with inputs from
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of the top departments in the world that conduct exciting research in the intersection of Design, Technology, Human-Computer Interaction, and Social Sciences and Humanities. In particular, the department aims