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- Czech Technical University in Prague
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or more publications in a journal with IF. The postdoc is expected to advance the state of the art in neuro-symbolic machine learning (i.e., learning combining the principles of deep learning and logical
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annotations for supervised learning is extremely challenging and consequently expensive. It is especially appealing in safety-critical applications of autonomous driving, where performance requirements
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to program distributed and shared memory algorithms, as well as use the new IPU technology. These algorithms will be applied to a range of applications ranging from machine learning for public health
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and spectroscopy, superresolution microscopy, DNA nanotechnology, plasmonics, and machine learning. The focus of the position will be on single-molecule fluorescence. The ideal candidate will have a
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your work to both theoreticians and experimentalist Advanced English (written and spoken) is a must Well-developed research skills, willingness to learn new techniques and approaches and eager to explore
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processing techniques, supported by artificial intelligence and machine learning algorithms, to streamline the interpretation of spectroscopic data. The research will be carried out in close collaboration
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, thoroughness, and responsibility Good organizational and communication skills Ability to work both independently and in a team High level of work commitment Strong willingness to learn new things continuously
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to learn new techniques and develop new approaches are able to work in an international team and collaborate with other researchers and students have an active attitude to problem-solving
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whole produce excellent publications, participate in dissemination and networking activities, and in establishing a new childhood studies environment optionally: teach / supervise students (in English or
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modeling. State estimation will employ control engineering methods, such as Kalman filters, and may alternatively explore cutting-edge machine learning techniques. The development of models and estimation