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system configurations, experimental setups and operational data. The scientific ambition is to develop methods that combine physical models and data-driven approaches for adaptive, real-time operation of
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architectures for quantum superlattice devices, including embedded contacts and electron-transparent regions. Develop transfer and stacking processes for graphene, hBN, transition metal dichalcogenides and charge
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learning methods for medical image analysis, with a particular focus in anomaly detection and unsupervised learning. In this position, you will have the chance to explore basic machine learning research as
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validate digital-twin and optimization methods for electrolysis systems, working both independently and collaboratively with the group and with academic and industrial partners. In particular, you will
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the position. Your work tasks You will develop and validate digital-twin and optimization methods for electrolysis systems, working both independently and collaboratively with the group and with academic and
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be to develop wireless sensing and communication methods that are designed together with AI-based inference, rather than treating connectivity as a separate layer. Particular attention will be given
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are looking for candidates interested in developing new machine learning methods for medical image analysis, with a particular focus in anomaly detection and unsupervised learning. In this position, you will
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. The project description must include: a presentation of an original research question a description of the initial theoretical framework and method a presentation of the proposed empirical material a work plan
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PhD position in detector development for the next generation of cryogenic dark-matter axion exper...
doctoral program at CERN, and will have access to experimental facilities at both institutions. The position includes a mobility component of up to two years at CERN, undertaken within CERN's Technology
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be to develop wireless sensing and communication methods that are designed together with AI-based inference, rather than treating connectivity as a separate layer. Particular attention will be given