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, statistics, data science, and public health. The goal is to develop new methods that allow researchers to learn from sensitive health data without compromising individual privacy. Using unique, nationwide
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Do you bring together domain specific knowledge from the social sciences with a genuine, hands-on command of AI and/or digital methods? Would you like to advance research that integrates state
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’ Cognitive Autonomy in Human-AI Interaction’). Research objectives This project develops methods to detect changes in users' cognitive load and designs digital interventions that support cognitive autonomy in
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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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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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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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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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support recipe optimisation for well-controlled materials and processes, you will investigate whether similar methods can be applied to mechanical recycling from heterogeneous waste streams. The ambition is
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of novel methods to decompose the heterogeneity of psychiatric disorders, particularly ADHD, using large-scale genetic, imaging, and epidemiological data from Denmark and other countries. You will apply
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related qualitative methods, the PhD fellow will explore how students experienced, negotiated, and articulated dissatisfaction with schooling. The PhD fellow will contribute to the development