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). The emergence of data-driven techniques (broadly grouped under the term “machine learning”) challenges the traditional foundations of controls and represents an alternative paradigm that cannot be ignored
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collaborative mindset and bring the following qualifications: a PhD degree in mathematics, with a focus on algebraic geometry; experience with at least some of the following: Fourier—Mukai transforms, Hochschild
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; experience with AI and machine learning methods, especially in the areas of natural language processing or graph neural networks; the ability to work independently and collaboratively in an interdisciplinary
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programming, statistics, machine learning and big data approaches in the context of soil-vegetation-atmosphere interactions excellent writing and oral communication skills in English and strong ambition
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(UQ) for machine learning and its validation. Your areas of research will be chosen based on both your own expert judgement and insight into trends and developments and on team requirements to ensure
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, ballistocardiography, and bio-radar) in combination with machine learning based algorithms for time series analysis into the whole OSA diagnosis and treatment pathway. During diagnosis unobtrusive sensors that can be
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investigate how machine-learning based algorithms can be used to personalize the user experience. The goal of this personalized user experience is to enable each individual user to discover their own
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for this position will have the following qualifications/qualities A PhD degree in either machine learning or computational molecular sciences. Advanced knowledge in molecular machine learning. Advanced knowledge in
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Your job We are looking for an enthusiastic postdoctoral researcher to expand and strengthen research on "Healthy Lifestyle through Data Science, Machine Learning, and AI". Are you excited about the
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develop a simplified model focusing on the leader stage. You will: Analyze experimental data and microscopic simulations Identify relevant physical features and parameters Apply machine learning techniques