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. The central aim is to design intelligent systems that dynamically adapt the environment to support optimal learning conditions, based on real-time neurophysiological feedback. Key principles guiding
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, sensing and optimization of the electromechanical aspects of industrial machines, with an emphasis on Industry 4.0 technologies such as machine vision, AI or digital twins. A digital twin can be defined as
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model of the ultrasound-sensitive hippocampal formation as environment. The candidate will develop surrogate models of the hippocampus to allow for more extensive optimization of the controller. Finally
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-guided therapy provides a promising line of treatment for PXE and related EC disorders. First, we will optimize the detection of pathogenic variants that are missed by current screening approaches. We will
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radar-based sensing. You will develop adaptive algorithms that dynamically select the optimal radio configuration based on the specific requirements of each application. The radio platform supports a
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your knowledge and skills on state-of-the-art in machine learning, (probabilistic) modelling, system identification and numerical optimization. How to apply Send your CV containing one or more references
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-of-the-art in machine learning, (probabilistic) modelling, system identification and numerical optimization. How to apply Send your CV, containing one or more references, a copy of your diploma (if already in
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monitoring of different sources of N fluxes in the system. He/she will need to contribute to the optimization and implementation of advanced sensing platforms for the collection of data on soil N, more
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/she will need to contribute to the optimization and implementation of advanced sensing platforms for the collection of data on different forms of nitrogen, more specifically nitrogen in manure and bio
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preclinical experiments on ex vivo brain slices and in vivo rodent models to investigate and optimize the effects of TIS Analyze ex vivo and in vivo electrophysiological and fMRI imaging datasets Collaborate