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algorithms and all the way to areas of use cases/applications of quantum algorithms in chemistry and life-science. Applicants can have a background in Computer Science, Physics, Chemistry, Mathematics
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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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School. Please note that this process will be initiated by the PhD School only after an employment offer has been made. The PhD programme must be completed in accordance with the Danish Ministerial Order
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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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, cluster structures, flat surfaces, and spectral networks. The goal of the project is to build new far-reaching connections between algebraic geometry, dynamical systems, and mathematical physics using a
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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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tackle the socio-techno-economical complexity of the built environment. Your vision is to close the “performance gap” by integrating Advanced HVAC System Design & Control, Cyber-Physical Modeling, and
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, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph representation learning. Programming skills