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challenge in this aging era, undermining the functionality, safety and longevity of critical steel infrastructure. There is an urgent need to advance monitoring, diagnosis and prognosis methods that can
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methods and examine challenges concerning both private and public sectors. It is our objective to become a world class leader in excellent research and high-quality teaching. We further strive to provide
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candidate therefore has a high academic level and a good grasp of research designs and methods. Strong computational skills are necessary. The application deadline is 15 September 2026, at 11.59 PM/23.59
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variability near the Faroe Islands and downstream ocean and climate changes around Denmark, as well as researchers working with statistical early-warning methods and Earth system modelling. Together
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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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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