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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
quality. Secondly, different machine learning strategies based on traditional supervised learning techniques (e. g. random forest (RF), artificial neural network (ANN)) will be applied using the parameters
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to work independently as well as in teams Work in a structured way, set goals and make plans to achieve them, result-oriented Excellent analytical skills, analyze data, assess different perspectives and
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algorithms to extract clinical indices and provide new digital biomarkers for sleep medicine. The aim of the project is to develop new algorithms and tools of Digital Health for non-invasive, home-based sleep
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are differentially private algorithms for statistical model parameter estimation under different trust relations. About the project The position is funded by the Norwegian Research Center for AI Innovation and will be
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optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware
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optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised learning techniques (e.g
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different EU countries. All 15 Ph.d. projects are within the overall theme of neuromorphic computing and analog signal processing, targeting applications in the fields of communication, sensing
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universities, one research center and two companies. The project has partners from eight different EU countries. All 15 Ph.d. projects are within the overall theme of neuromorphic computing and analog signal
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temporal patterns across different neurons in the neocortical circuit and use them for closed-loop brain stimulation. By examining how these spatiotemporal dynamics relate to behaviour, you will develop new
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work will focus on identifying the mathematical knowledge and properties to guide hardware optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g