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-principles physical knowledge with data-driven learning to enable continuous, autonomous system oversight. Research objectives The project pursues two interconnected research directions: Continuous
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PhD Scholarship in University Students’ Learning Via Digital Twins of Neutron Scattering Instruments
studies and data analysis with one of the leading groups in the field. Qualified applicants must have: An MSc degree in Physics, Nanoscience, Chemistry, Engineering, or Material Science, or Educational
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learning spaces can bridge the on-campus social learning moment with extended, individualised learning in other temporal and physical contexts. The project is centrally concerned with how generative AI tools
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, Water Resources, Civil Engineering, Physics and Meteorology, Applied Mathematics, Computer Science/Engineering, or a comparable discipline. The MSc degree must be equivalent with the Danish MSc degree
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PhD Scholarship in Development of Cement-Free Living Building Materials for Sustainable Construction
(MICP) technology (preferred) Knowledge of how microbes interact and affect and are affected by their physical-chemical environment (preferred) Experience in biomaterials, polymer science, and hydrogel
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degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph
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. The further process After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the hiring committee. All applicants are then
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of the jobpost. Further information Read more about our recruitment process here. The assessment of candidates for the position will be carried out by qualified experts. Shortlisting will be applied. This means
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. Our mission encompasses the creation of insights that allow the development of context-aware, distributed, and embedded cyber-physical systems, with a particular focus on Internet-of-Things (IoT) and
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screening and other interventions have been implemented. The project views recruitment as a multi-stage process, where screening, interviews and final decisions interact, and where bias-reducing mechanisms